Minutiae Extraction from Fingerprint Images - a Review
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paper_title: Fingerprint Image Enhancement using Filtering Techniques

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Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. Most of the minutiae detection methods are based on image binarization while some others extract the minutiae directly from gray-scale images. In this work we compare these two approaches and propose two different methods for fingerprint ridge image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method uses a unique anisotropic filter for direct gray-scale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of time required and efficiency.
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paper_title: A robust approach to image enhancement based on fuzzy logic

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In this paper, we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement: (i) removing impulse noise, (ii) smoothing out nonimpulse noise, and (iii) enhancing (or preserving) edges and certain other salient structures. We derive three different filters for each of the above three tasks using the weighted (or fuzzy) least squares (LS) method, and define the criteria for selecting each of the three filters. The criteria are based on the local context, and they constitute the antecedent clauses of the fuzzy rules. The overall result of the fuzzy rule-based system is the combination of the results of the individual filters, where each result contributes to the degree that the corresponding antecedent clause is satisfied. This approach gives us a powerful and flexible image enhancement paradigm. Results of the proposed method on several types of images are compared with those of other standard techniques.
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paper_title: Latent Fingerprint Matching: Fusion of Manually Marked and Derived Minutiae

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Matching unknown latent fingerprints lifted from crime scenes to full (rolled or plain) fingerprints in law enforcement databases is of critical importance for combating crime and fighting terrorism. Compared to good quality full fingerprints acquired using live-scan or inking methods during enrollment, latent fingerprints are often smudgy and blurred, capture only a small finger area, and have large nonlinear distortion. For this reason, features (minutiae and singular points) in latents are typically manually marked by trained latent examiners. However, this introduces an undesired interoperability problem between latent examiners and automatic fingerprint identification systems (AFIS); the features marked by examiners are not always compatible with those automatically extracted by AFIS, resulting in reduced matching accuracy. While the use of automatically extracted minutiae from latents can avoid interoperability problem, such minutiae tend to be very unreliable, because of the poor quality of latents. In this paper, we improve latent to full fingerprint matching accuracy by combining manually marked (ground truth) minutiae with automatically extracted minutiae. Experimental results on a public domain database, NIST SD27, demonstrate the effectiveness of the proposed algorithm.
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paper_title: A Fingerprint Minutiae Recognition System Based on Genetic Algorithms

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In this paper, we propose a fingerprint verification system using two different modules: the automatic classification of fingerprints which is based on the minutiae-matching algorithms and the verification-search technique which is based on genetic algorithms. Our experiments on a large set of fingerprint images show that our approach is highly promising. Its performance shows a great flexibility in recognizing a person very quickly with an error-prone of at most 1%.
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paper_title: Fingerprint Biometric-based Self-Authentication and Deniable Authentication Schemes for the Electronic World

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AbstractThis paper presents fingerprint biometric-based self-authentication and deniable authentication schemes, which enable a receiver to identify source of the message but not to prove the identity of the sender to a third party. At first, we identify the security flaws of some recently proposed deniable authentication schemes by three application scenarios, and prove that those schemes are vulnerable to impersonation and eavesdropping attacks. For remedy, we propose two-factor-based self-authentication and deniable authentication schemes in which, firstly, each user has to perform self-authentication by something he knows (password) and something he is (biometrics), and then he can accomplish the transaction, e.g., e-commerce. Security analysis shows that the proposed schemes can overcome the security pitfalls of the previous schemes and are secure, reliable and practicable with high potential to be used in the electronic world.
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paper_title: Fingerprint recognition using standardized fingerprint model

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Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models...are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we use the final fingerprint to match with others in FVC2004 fingerprint database (DB4) to show the capability of the model.
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paper_title: On the individuality of fingerprints

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Fingerprint identification is based on two basic premises: (1) persistence and (2) individuality. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae features to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of a false correspondence between minutiae-based representations from two arbitrary fingerprints belonging to different fingers. Our results show that (1) contrary to the popular belief, fingerprint matching is not infallible and leads to some false associations, (2) while there is an overwhelming amount of discriminatory information present in the fingerprints, the strength of the evidence degrades drastically with noise in the sensed fingerprint images, (3) the performance of the state-of-the-art automatic fingerprint matchers is not even close to the theoretical limit, and (4) because automatic fingerprint verification systems based on minutia use only a part of the discriminatory information present in the fingerprints, it may be desirable to explore additional complementary representations of fingerprints for automatic matching.
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paper_title: DWT Based Fingerprint Recognition using Non Minutiae Features

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Forensic applications like criminal investigations, terrorist identification and National security issues require a strong fingerprint data base and efficient identification system. In this paper we propose DWT based Fingerprint Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3 level DWT. The Dominant local orientation angle θ and Coherence are computed on LL band only. The Centre Area Features and Edge Parameters are determined on each DWT level by considering all four sub bands. The comparison of test fingerprint with database fingerprint is decided based on the Euclidean Distance of all the features. It is observed that the values of FAR, FRR and TSR are improved compared to the existing algorithm.
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paper_title: A new fuzzy logic based image enhancement.

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In this paper a new fuzzifier (fh) and a new contrast intensification operator (NINT) have been proposed for the enhancement of computerized tomography (CT) images. The performance of the proposed technique has been compared with an existing fuzzy logic based enhancement technique in terms of a new fuzzy contrast measure. The superiority of the proposed technique has been established.
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paper_title: A Detail-Preserving Type-2 Fuzzy Logic Filter for Impulse Noise Removal from Digital Images

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A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.
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paper_title: An identity-authentication system using fingerprints

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Fingerprint verification is an important biometric technique for personal identification. We describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction algorithm that is faster and more accurate than our earlier algorithm (1995). An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA I workstation (it is expected to run as fast or faster on a 200 HMz Pentium).
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paper_title: Image filter design for fingerprint enhancement

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Fingerprint filter design and a method of enhancing fingerprint image are discussed. Two distinct filters in the Fourier domain are designed, a frequency filter corresponding to ridge frequencies and a direction filter corresponding to ridge directions on the basis of fingerprint ridge properties. An energy function for selecting image features, i.e. frequencies and directions, is defined by intensities of images obtained with the above filters and a measure of smoothness of the features. Using the image features which minimize the energy function, an enhanced image is produced from the filtered images. This image enhancement method is applied to fingerprint matching. In experiments with rolled prints, it is shown that the matching error rate is reduced by about two-thirds compared with Asai's method.
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paper_title: A neural network fingerprint segmentation method

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In this work a computational segmentation method, applied to the detection of the region of interest on fingerprint images, is proposed. The approach is based on the hypothesis that a small fingerprint fragment resembles a two-dimension sinusoid function. Therefore, its Fourier spectrum must present a well-defined pattern. Since neural networks are very suitable for solving pattern recognition problems, an MLP network is used to discriminate the regions containing fingerprint fragments from the rest of the image. The proposed model is tested over fingerprint images obtained from the NIST special database 27, and the obtained results demonstrate that the approach works reasonably well for images with different noise and contrast levels.
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paper_title: Fuzzy techniques in image processing

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Vision in general and images in particular have always played an important and essential role in human life. Today, image processing is a very active research area with many applications. In order to cope with the wide variety of image processing problems, several techniques have been introduced and developed, quite often with great success. Among the different techniques that are currently in use, we also encounter fuzzy techniques. The use of fuzzy techniques in image processing is one of the main topics of the Fuzziness and Uncertainty Modelling Research Group of Prof. Kerre. In this paper, we briefly summarize some achievements of the past years.
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paper_title: Reconstructing Orientation Field From Fingerprint Minutiae to Improve Minutiae-Matching Accuracy

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Minutiae are very important features for fingerprint representation, and most practical fingerprint recognition systems only store the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. In this paper, we propose a novel algorithm to use minutiae for fingerprint recognition, in which the fingerprint's orientation field is reconstructed from minutiae and further utilized in the matching stage to enhance the system's performance. First, we produce "virtual" minutiae by using interpolation in the sparse area, and then use an orientation model to reconstruct the orientation field from all "real" and "virtual" minutiae. A decision fusion scheme is used to combine the reconstructed orientation field matching with conventional minutiae-based matching. Since orientation field is an important global feature of fingerprints, the proposed method can obtain better results than conventional methods. Experimental results illustrate its effectiveness.
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paper_title: Fingerprint indexing based on novel features of minutiae triplets

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We are concerned with accurate and efficient indexing of fingerprint images. We present a model-based approach, which efficiently retrieves correct hypotheses using novel features of triangles formed by the triplets of minutiae as the basic representation unit. The triangle features that we use are its angles, handedness, type, direction, and maximum side. Geometric constraints based on other characteristics of minutiae are used to eliminate false correspondences. Experimental results on live-scan fingerprint images of varying quality and NIST special database 4 (NIST-4) show that our indexing approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion, and clutter. We also perform scientific experiments to compare the performance of our approach with another prominent indexing approach and show that the performance of our approach is better for both the live scan database and the ink based database NIST-4.
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paper_title: A fingerprint recognizer using fuzzy evolutionary programming

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A fingerprint recognition system is built with two principal components: the fingerprint administrator and the fingerprint recognizer. Fingerprints are identified by their special features, such as ridge endings, ridge bifurcation, short ridges and ridge enclosures, which are collectively called the minutiae. The fingerprint administrator uses the method of gray-scale ridge tracing backed up by a validating procedure to extract the minutiae of fingerprints. The fingerprint recognizer employs the technique of fuzzy evolutionary programming to match the minutiae of an input fingerprint with those from a database. Experimental results show the methods used are highly effective.
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paper_title: Image Retrieval System Based on Adaptive Color Histogram and Texture Features

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This paper proposes three image features for use in image retrieval. The first image feature is based on color distribution and is called an adaptive color histogram (ACH). The second and third image features, called adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM), are based on color and texture features, respectively. ACH uses three steps, including cluster drop, auto cluster merge and auto cluster split to improve the K-mean algorithm. AMCOM is commonly used and is a generally conventional AMCOM. GHAM estimates the average gradient of pixels in a motif of pattern blocks and creates a histogram of the average gradient corresponding to motifs of pattern blocks. We integrate ACH, AMCOM and GHAM to form an AMCGH system to facilitate a more accurate and efficient image retrieval system. To present the performance of the proposed system, four different image databases, including gray texture images, two types of color texture images and color natural images, are tested and analyzed.
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paper_title: Fingerprint Image Enhancement: Algorithm and Performance Evaluation

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In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
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paper_title: Fingerprint Matching Incorporating Ridge Features With Minutiae

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This paper introduces a novel fingerprint matching algorithm using both ridge features and the conventional minutiae feature to increase the recognition performance against nonlinear deformation in fingerprints. The proposed ridge features are composed of four elements: ridge count, ridge length, ridge curvature direction, and ridge type. These ridge features have some advantages in that they can represent the topology information in entire ridge patterns existing between two minutiae and are not changed by nonlinear deformation of the finger. For extracting ridge features, we also define the ridge-based coordinate system in a skeletonized image. With the proposed ridge features and conventional minutiae features (minutiae type, orientation, and position), we propose a novel matching scheme using a breadth-first search to detect the matched minutiae pairs incrementally. Following that, the maximum score is computed and used as the final matching score of two fingerprints. Experiments were conducted for the FVC2002 and FVC2004 databases to compare the proposed method with the conventional minutiae-based method. The proposed method achieved higher matching scores. Thus, we conclude that the proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used in conjunction with existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.
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paper_title: Filterbank-based fingerprint matching

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Biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
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paper_title: On-Line Fingerprint Verification

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Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.
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paper_title: New Fingerprint Image Enhancement Using Directional Filter Bank

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This paper describes a new method of directional filter-based analysis for fingerprint enhancement. Fingerprint images can be represented by a directional field of regular structure of ridge patterns. The dominant directional component of ridge plays a very important role in the pre-processing steps of fingerprint image analysis such as ridge's linking and noise removal for minutiae extraction. A directional filter bank analyzes an input image into directional subband images and synthesizes them to the perfectly reconstructed image. In this paper, a new fingerprint enhancement algorithm based on a directional filter bank is proposed. The algorithm decomposes a fingerprint image into directional subband images in the analysis stage, processes the subband images in the processing stage, and reconstructs them as the enhanced image in the synthesis stage. The experiment results show that the proposed method reduces the influence of noise on the ridges and valleys, enhances the ridge’s moving shape, and preserves the spatial characteristics at minutiae and singular points.
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paper_title: Fingerprint Image Enhancement Using Type-2 Fuzzy Sets

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High-quality images are a prerequisite for accurate matching of fingerprint images. However, fingerprint images are rarely of perfect quality. They may be degraded or corrupted due to variations in skin and impression conditions. Thus, fingerprint images must be enhanced before use. In this work, we demonstrate the efficacy of applying Type-2 fuzzy logic to fingerprint image enhancement when the input is pre-processed by the Hong’s algorithm. We have measured the fuzzy quality visually and quantitatively to note the effectiveness of the proposed technique for enhancement.
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paper_title: Image quality measures for fingerprint image enhancement

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Fingerprint image quality is an important factor in the performance of Automatic Fingerprint Identification Systems(AFIS). It is used to evaluate the system performance, assess enrollment acceptability, and evaluate fingerprint sensors. This paper presents a novel methodology for fingerprint image quality measurement. We propose limited ring-wedge spectral measure to estimate the global fingerprint image features, and inhomogeneity with directional contrast to estimate local fingerprint image features. Experimental results demonstrate the effectiveness of our proposal.
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paper_title: Fingerprint Classification by Directional Image Partitioning

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In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into "homogeneous" connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness.
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paper_title: Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance

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This paper presents a new approach to enhancing feature extraction for low-quality fingerprint images by adding noise to the original signal. Feature extraction often fails for low-quality fingerprint images obtained from excessively dry or wet fingers. In nonlinear signal processing systems, a moderate amount of noise can help amplify a faint signal while excessive amounts of noise can degrade the signal. Stochastic resonance (SR) refers to a phenomenon where an appropriate amount of noise added to the original signal can increase the signal-to-noise ratio. Experimental results show that Gaussian noise added to low-quality fingerprint images enables the extraction of useful features for biometric identification. SR was applied to 20 fingerprint images in the FVC2004 DB2 database that were rejected by a state-of-the-art fingerprint verification algorithm due to failures in feature extraction. SR enabled feature extraction from 10 out of 11 low-quality images with poor contrast. The remaining nine images were damaged fingerprints from which no meaningful features can be obtained. Improved feature extraction using SR decreases an equal error rate of fingerprint verification from 6.55% to 5.03%. The receiver operating characteristic curve shows that the genuine acceptance rates are improved for all false acceptance rates.
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paper_title: Fingerprint enhancement by directional Fourier filtering

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A new method of enhancing fingerprint images is described, based upon nonstationary directional Fourier domain filtering. Fingerprints are first smoothed using a directional filter whose orientation is everywhere matched to the local ridge orientation. Thresholding then yields the enhanced image. Various simplifications lead to efficient implementation on general-purpose digital computers. Results of enhancement are presented for fingerprints of various pattern classifications. A comparison is made with the enhancement used within the automated fingerprint identification system (AFIS) developed by the UK Home Office. Use of the proposed enhancement method leads to significant improvements in the speed and accuracy of the AFIS.
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paper_title: A topology-based matching algorithm for fingerprint authentication

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A proper representation of the ridge pattern on a fingerprint is addressed. The representation must be such that it supports fingerprint matching. A nonink fingerprint input device using an optical prism is considered. A ridge pattern based on a structural model of minutiae is developed. Finally, a fingerprint matching method via tree matching is given. This method is shown to be invariant under translation and rotation, and it does not depend on the use of a prespecified core point. Experimental results indicate that this matching method is quite feasible. However, minutiae associated with whorls and noisy regions are troublesome and unstable. >
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paper_title: A Novel Framework for Enhancing Images Corrupted by Impulse Noise Using Type-II Fuzzy Sets

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This paper presents a novel type-II fuzzy filter to remove impulse noise in an image. The filter processes impulses as type-II fuzzy sets. Type-II fuzzy sets model uncertainties more effectively than type-I fuzzy sets because the membership function for a type-I fuzzy set for a particular input is a crisp value. The proposed algorithm firstly detects impulses by considering grayscale distribution amongst neighbouring pixels and then determines the presence of impulsive pixels by comparing it with a range of threshold values using an S - shaped fuzzy membership function that is itself fuzzy. As the level of contamination varies from pixel to pixel, the modified value for the noisy pixel is calculated depending on the impulse noise present in it. The better performance of the filter is demonstrated on the basis of PSNR values calculated from the original and restored images respectively.
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paper_title: Evolutionary Algorithms Application Analysis in Biometric Systems

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Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authentication process simplification in computer systems has raised significant attention to reliability and efficiency of biometric systems. Modern biometric systems still face many reliability and efficiency related issues such as reference database search speed, errors while recognizing of biometric information or automating biometric feature extraction. Current scientific investigations show that application of evolutionary algorithms may significantly improve biometric systems. In this article we provide a comprehensive review of main scientific research done in sphere of evolutionary algorithm application for biometric system parameter improvement.
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paper_title: Fingerprint image processing using neural networks

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A system for minutiae extraction in fingerprint images using back-propagation networks and Gabor filters is described. Fingerprint images are first convolved with complex Gabor filters and the resulting phase and magnitude signals are passed to networks to identify minutia regions. Promising results are obtained with good detection ratio and low false alarm rate. The importance of having good representations of image data to neural networks is illustrated through variations in performance of different trained networks. The usefulness of Gabor filters in textural image processing and neural networks in image feature extraction is demonstrated. >
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paper_title: Combining features for distorted fingerprint matching

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Extracting and fusing discriminative features in fingerprint matching, especially in distorted fingerprint matching, is a challenging task. In this paper, we introduce two novel features to deal with nonlinear distortion in fingerprints. One is finger placement direction which is extracted from fingerprint foreground and the other is ridge compatibility which is determined by the singular values of the affine matrix estimated by some matched minutiae and their associated ridges. Both of them are fixed-length and easy to be incorporated into matching score. In order to improve the matching performance, we combine these two features with orientation descriptor and local minutiae structure, which are used to measure minutiae similarity, to achieve fingerprint matching. In addition, we represent minutiae set as a graph and use graph connect component and iterative robust least square (IRLS) to detect creases and remove spurious minutiae close to creases. Experimental results on FVC2004 DB1 and DB3 demonstrate that the proposed algorithm could obtain promising results. The equal error rates (EER) are 3.35% and 1.49% on DB1 and DB3, respectively.
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paper_title: Handbook of Fingerprint Recognition

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A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.
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paper_title: Fingerprint Image Enhancement: Algorithm and Performance Evaluation

paper_content:

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
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paper_title: Fingerprint Image Enhancement: Algorithm and Performance Evaluation

paper_content:

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.
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paper_title: On-Line Fingerprint Verification

paper_content:

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.
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paper_title: Segmentation of Fingerprint Images

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An important step in an automatic fingerprint recognition system is the segmentation of fingerprint images. The task of a fingerprint segmentation algorithm is to decide which part of the image belongs to the foreground, originating from the contact of a fingertip with the sensor, and which part to the background, which is the noisy area at the borders of the image. In this paper, an algorithm for the segmentation of fingerprints is presented. The method uses three pixel features, being the coherence, the mean and the variance. An optimal linear classifier is trained for the classification per pixel, while morphology is applied as postprocessing to obtain compact clusters and to reduce the number of classification errors. Manual inspection shows that the proposed method provides accurate high-resolution segmentation results. Only 6.8% of the pixels is misclassified while the postprocessing further reduces this ratio. Experiments show that the proposed segmentation method and manual segmentation perform equally well in rejecting false fingerprint features from the noisy background. Keywords— Image processing, fingerprint image segmentation, pixel features, coherence, linear classification, neural network.
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paper_title: Achievements and Challenges in Fingerprint Recognition

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This chapter gives an overview of fingerprint recognition. First, a number of applications of fingerprint recognition are presented, including physical access control, computer login, key-less lockers, and biometrie smart cards. This is followed by an overview of current fingerprint recognition technology and algorithms, introducing various new methods, technical challenges, and directions on how to solve them. Finally, this chapter shows how these new methods can be used to improve the performance of the selected applications.
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paper_title: Fingerprint recognition in low quality images

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Abstract Algorithms are identified which are best suited for an automatic fingerprint recognition system operating on low quality images. New preprocessing algorithms for noise removal and binarization are described. Three approaches to classification are investigated: a correlation classifier, and two feature-based classification schemes. The best results on a database of 80 fingerprints are obtained with spatial-frequency features. Three classifiers (neural net, linear classifier and nearest neighbour) using these features are successful in identifying an independent test set. Details of the results are shown. In conclusion suggestions are made concerning the most suitable algorithms in each of the processing steps.
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paper_title: A cost-effective fingerprint recognition system for use with low-quality prints and damaged fingertips

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Abstract The development of a robust algorithm allowing good recognition of low-quality fingerprints with inexpensive hardware is investigated. A threshold FFT approach is developed to simultaneously smooth and enhance poor quality images derived from a database of imperfect prints. Features are extracted from the enhanced images using a number of approaches including a novel wedge ring overlay minutia detector that is particularly robust to imperfections. Finally, a number of neural net and statistically based classifiers are evaluated for the recognition task. Results for various combinations of the process are presented and discussed with regard to their utility in such a system.
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paper_title: Pores and Ridges: Fingerprint Matching Using Level 3 Features

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Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~1000 dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most automated fingerprint identification systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement (1998). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000 dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combination with Level 1 and 2 features
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paper_title: A fingerprint recognition algorithm combining phase-based image matching and feature-based matching

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, This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. The use of Fourier phase information of fingerprint images makes possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. Experimental evaluations using two different types of fingerprint image databases demonstrate efficient recognition performance of the proposed algorithm compared with a typical minutiae-based algorithm and the conventional phase-based algorithm.
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paper_title: Normalized Cross-Correlation Based Fingerprint Matching

paper_content:

To perform fingerprint matching based on the number of corresponding minutia pairings, has been in use for quite sometime. But this technique is not very efficient for recognizing the low quality fingerprints. To overcome this problem, some researchers suggest the correlation technique which provides better result. Use of correlation-based methods is increasing day-by-day in the field of biometrics as it provides better results. In this paper, we propose normalized cross-correlation technique for fingerprint matching to minimize error rate as well as reduce the computational effort than the minutiae matching method. The EER (equal error rate) obtained from result till now with minutiae matching method is 3%, while that obtained for the method proposed in this paper is approx 2% for all types of fingerprints in combined form.
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paper_title: A Context-Aware Similarity Search for a Handwritten Digit Image Database

paper_content:

In this paper, we propose a context-aware similarity search algorithm for a handwritten digit image database. Though we apply our algorithm to the search of handwritten digit images, the devised technique is generally applicable to other types of content-based image retrieval (CBIR). One of the central problems regarding CBIR is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms that are used in CBIR have used the Minkowski metric (or Lp-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our new search algorithm tackles this problem by employing a new similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in an image collection. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.
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paper_title: Local Correlation-based Fingerprint Matching

paper_content:

Most fingerprint matching systems are based on matching minutia points between two fingerprint images. Each minutia is represented by a fixed number of attributes such as the location, orientation, type and other local information. A hard decision is made on the match between a pair of minutiae based on the similarity of these attributes. In this paper, we present a minutiae matching algorithm that uses spatial correlation of regions around the minutiae to ascertain the quality of each minutia match. The proposed algorithm has two main advantages. Since the gray level values of the pixels around a minutia point retain most of the local information, spatial correlation provides an accurate measure of the similarity between minutia regions. Secondly, no hard decision is made on the correspondence between a minutia pair. Instead the quality of all the minutiae matches are accumulated to arrive at the final matching score between the template and query fingerprint impressions. Experiments on a database of 160 users (4 impressions per finger) indicate that the proposed algorithm serves well to complement the 2D dynamic programming based minutiae matching technique; a combination of these two methods can reduce the false non-match rate by approximately 3.5% at a false match rate of 0.1%.
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paper_title: Correlation-based fingerprint matching using FPGAs

paper_content:

Correlation based methods are gaining attention in the biometric field due to the extremely good results achieved for pattern matching recognition in authentication and verification processes. In particular, a high matching accuracy can be obtained with these methods in the fingerprint field. However, they cannot be used in many applications because of the large computational effort required. On the other hand, they are very well suited for FPGA implementations. In this work we present efficient architectures for implementation of zero mean normalized cross correlation using Virtex-4 FPGAs for application to correlation based fingerprint matching. In particular, we compare the implementations of correlation in the spatial and Fourier domains. Experimental results demonstrate that FPGAs allow improving performance by at least two orders of magnitude with respect to software implementations in a modern personal computer. Thanks to this speedup, correlation based algorithms for fingerprint matching achieve acceptable performance for real-time processing
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paper_title: Run-based algorithms for binary image analysis and processing

paper_content:

In this paper we suggest a variant of a binary image representation based on run length encoding. This variant allows one to build a "graph representation" for a number of computing tasks like component labeling, computations of Euler number, diameter and convex hull, and the detection of local extrema and multiple points. Finally, a running application in the raster-to-vector conversion of digital maps is provide.
---
paper_title: Pipelined minutiae extraction from fingerprint images

paper_content:

Fingerprint identification is an important biometric technique for personal identification. Most of the automatic systems of fingerprint recognition are based on matching of fingerprint minutiae. In this paper, we described a method that reduces the complexity of computations and extract the minutiae from binary image with appropriate speed. In this paper a new hardware scheme is presented which is based on pipelined architecture in order to real-time minutiae extraction. Simulations and implementation result shows the operation and accuracy of suggested method.
---
paper_title: Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition

paper_content:

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs.
---
paper_title: Fingerprint local analysis for high-performance minutiae extraction

paper_content:

The paper presents a novel approach to identify the minutiae present in a fingerprint image based on the analysis of the local properties. The typical patterns of minutiae called ridge termination and bifurcation are identified by studying the intensity along squared paths in the image. The presented algorithm works both on grey-level image and binarized image and, despite its simplicity, it achieves good accuracy and it can be a good candidate to be implemented in hardware or executed on simple hardware architectures, for example in biometric systems embedded in portable applications such as cellular phones and smart cards.
---
paper_title: A Chaincode Based Scheme for Fingerprint Feature Extraction Abstract

paper_content:

A feature extraction method using the chaincode representation of fingerprint ridge contours is presented. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. The direction field is estimated from a set of selected chaincodes. The original gray-scale image is enhanced using a dynamic filtering scheme that takes advantage of the estimated direction flow of the contours. Minutiae are generated using ridge contour following.
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paper_title: Detecting fingerprint minutiae by run length encoding scheme

paper_content:

Many approaches to minutiae extraction have already been proposed for automatic fingerprint matching, and most transform fingerprint images into binary images through state-of-the-art algorithms and submit the binary image to a thinning process. However, this paper proposes an original technique for extracting minutiae based on representing the ridge structure of a fingerprint image as a run length code (RLC). The essential idea is to detect minutiae by searching for the termination points or bifurcation points of ridges in the RLC, rather than in a fingerprint image. Experimental results and a comparative analysis show that the proposed method is fairly reliable and faster than a conventional thinning-based method.
---
paper_title: A Chaincode Based Scheme for Fingerprint Feature Extraction Abstract

paper_content:

A feature extraction method using the chaincode representation of fingerprint ridge contours is presented. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. The direction field is estimated from a set of selected chaincodes. The original gray-scale image is enhanced using a dynamic filtering scheme that takes advantage of the estimated direction flow of the contours. Minutiae are generated using ridge contour following.
---
paper_title: Fingerprint minutiae extraction from skeletonized binary images

paper_content:

Abstract Fingerprint comparison is usually based on minutiae matching. The minutiae considered in automatic identification systems are normally ridge bifurcations and terminations. In this paper we present a set of algorithms for the extraction of fingerprint minutiae from skeletonized binary images. The goal of the present work is the extraction of the real 40–60 minutiae of a fingerprint image from the 2000–3000 contained in typical skeletonized and binarized images. Besides classical methodologies for minutiae filtering, a new approach is proposed for bridge cleaning based on ridge positions instead of classical methods based on directional maps. Finally, two novel criteria and related algorithms are introduced for validating the endpoints and bifurcations. Statistical analysis of the results obtained by the proposed approach shows efficient reduction of spurious minutiae. The use of the fingerprint minutiae extraction algorithms has also been considered in a fingerprint identification system in terms of timing and false reject or acceptance rates. The presented minutiae extraction algorithm performs correctly in dirty areas and on the background as well, making computationally expensive segmentation algorithms unnecessary. The results are confirmed by visual inspections of validated minutiae of the NIST sdb 4 reference fingerprint image database.
---
paper_title: Run-based algorithms for binary image analysis and processing

paper_content:

In this paper we suggest a variant of a binary image representation based on run length encoding. This variant allows one to build a "graph representation" for a number of computing tasks like component labeling, computations of Euler number, diameter and convex hull, and the detection of local extrema and multiple points. Finally, a running application in the raster-to-vector conversion of digital maps is provide.
---
paper_title: Fingerprint image postprocessing: A combined statistical and structural approach

paper_content:

Abstract For complex patterns such as fingerprints, it is difficult to describe the dactylotype by using a set of feature vectors, as well as to construct and parse all of the individual fingerprint pattern with a syntactical approach. In this paper, which is different from the conventional methods, we present an approach in which the statistical and structural approaches are combined to represent the fingerprint false minutia patterns. Based on the proposed approach, a fingerprint image postprocessing algorithm is developed to eliminate the false minutiae in fingerprint images. Because a global minutia relation is considered on the whole image, experimental results demonstrate the advantages of the combined approach.
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paper_title: Neural network based minutiae filtering in fingerprints

paper_content:

Minutiae correspond essentially to the terminations and bifurcations of fingerprint patterns. Since the quality of fingerprint images is often low, automatic minutiae detection is a very difficult task and the extraction algorithms produce a large number of false alarms. We present an approach to minutiae filtering based on a neural network. The minutiae neighborhoods extracted by the algorithm presented by us (1997) are normalized with respect to rotation and scale, and their dimensionality is reduced via a KL transform. A neural classifier, whose topology has been designed to exploit the minutiae duality, is employed to perform the neighborhoods classification. The filtering proposed, as confirmed by simulations, allows a significant improvement in the overall performance to be achieved.
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paper_title: Learning Fingerprint Minutiae Location and Type

paper_content:

For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by ∼1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by ∼3.2% on our fingerprint database.
---
paper_title: Pipelined minutiae extraction from fingerprint images

paper_content:

Fingerprint identification is an important biometric technique for personal identification. Most of the automatic systems of fingerprint recognition are based on matching of fingerprint minutiae. In this paper, we described a method that reduces the complexity of computations and extract the minutiae from binary image with appropriate speed. In this paper a new hardware scheme is presented which is based on pipelined architecture in order to real-time minutiae extraction. Simulations and implementation result shows the operation and accuracy of suggested method.
---
paper_title: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

paper_content:

In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.
---
paper_title: Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition

paper_content:

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs.
---
paper_title: Hybrid fuzzy logic and neural network model for fingerprint minutiae extraction

paper_content:

This paper presents the research into the use of fuzzy-neuro technology in automated finger print recognition for the extraction of fingerprint features, known as minutiae. The work presented here is an addendum to work carried out earlier by Sagar et al. (1995).
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paper_title: Fingerprint local analysis for high-performance minutiae extraction

paper_content:

The paper presents a novel approach to identify the minutiae present in a fingerprint image based on the analysis of the local properties. The typical patterns of minutiae called ridge termination and bifurcation are identified by studying the intensity along squared paths in the image. The presented algorithm works both on grey-level image and binarized image and, despite its simplicity, it achieves good accuracy and it can be a good candidate to be implemented in hardware or executed on simple hardware architectures, for example in biometric systems embedded in portable applications such as cellular phones and smart cards.
---
paper_title: A novel method for fingerprint feature extraction

paper_content:

Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques [1] based on high-resolution fingerprints images of the individual. Fingerprints have been used in forensic as well as commercial applications for identification as well as verification. The fingerprint surface is made up of a system of ridges and valleys. The steps for Fingerprint recognition include image acquisition, preprocessing, feature extraction and matching [2]. In the present work, a new fingerprint feature detection algorithm has been proposed. It has been found that presence of noise in fingerprint images leads to spurious minutiae. To overcome this problem, feature extraction has been done which efficiently determine the minutiae points in fingerprint [1]. The proposed method can be used in matching the template for finding bifurcation and termination. The new smoothing algorithm is proposed for the detection of the features of fingerprints. A method has been introduced for finding ridges in the fingerprint image with the help of eight different masks. It is a process of making a binary image of ridges from the grayscale fingerprint image. The experimental results showed the accuracy of the algorithm in terms of genuine acceptance rate, false rejection rate, false acceptance rate.
---
paper_title: A Chaincode Based Scheme for Fingerprint Feature Extraction Abstract

paper_content:

A feature extraction method using the chaincode representation of fingerprint ridge contours is presented. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. The direction field is estimated from a set of selected chaincodes. The original gray-scale image is enhanced using a dynamic filtering scheme that takes advantage of the estimated direction flow of the contours. Minutiae are generated using ridge contour following.
---
paper_title: Fingerprint recognition using neural network

paper_content:

The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scaling. Simulation results are obtained with good detection ratio and low failure rate. The proposed method is found to be reliable for a system with a small set of fingerprint data. >
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paper_title: A Novel Approach for Fingerprint Matching Using Minutiae

paper_content:

Fingerprint recognition is one of the most important challenging tasks of the biometrics identification due to the fact that they are easy to obtain and most importantly they are reliable. A number of pattern recognition methods have been used to perform fingerprint matching. Among such methods fingerprint matching based on minutia features is one of the researched problems. So we proposed effective fingerprint matching algorithm based on feature extraction. Also we have presented a novel fingerprint recognition technique for minutia extraction and a minutia matching. For minutia marking we considered one special false minutiae removal method and for minutiae matching we considered alignment based method. Experimental results show that this method achieves a much better matching performance.
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paper_title: Novel Approaches for Minutiae Verification in Fingerprint Images

paper_content:

A majority of the existing fingerprint recognition algorithms are based on matching minutia features. Therefore, minutiae extraction is one of the critical steps in fingerprint verification algorithms. Poor quality fingerprint images lead to missing and spurious minutiae that degrade the performance of the matching system. We propose two new techniques for minutiae verification based on non-trivial gray level features. The proposed features intuitively represent the structural properties of the minutiae neighborhood leading to better classification. We use directionally selective steerable wedge filters to differentiate between minutiae and non-minutiae neighborhoods. We also propose a second technique based on Gabor expansion that results in even better discrimination. We present an objective evaluation of both the algorithms.
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paper_title: A novel method of fingerprint minutiae extraction based on Gabor phase

paper_content:

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.
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paper_title: An algorithm for fingerprint image postprocessing

paper_content:

Most of the current fingerprint identification and verification systems performs fingerprint matching based on different attributes of the minutia details present in fingerprints. The minutiae (i.e. ridge endings and ridge bifurcations) are usually detected in the thinned binary image of the fingerprint. Due to the presence of noise as well as the use of different preprocessing stages the thinned binary image contains a large number of false minutiae which may highly decrease the matching performance of the system. A new algorithm of fingerprint image postprocessing is proposed. The algorithm operates onto the thinned binary image of the fingerprint in order to eliminate the false minutiae. The proposed algorithm is able to detect and cancel the minutiae associated with most of the false minutia structures which may be encountered in the thinned fingerprint image.
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paper_title: Local Feature Extraction in Fingerprints by Complex Filtering

paper_content:

A set of local feature descriptors for fingerprints is proposed. Minutia points are detected in a novel way by complex filtering of the structure tensor, not only revealing their position but also their direction. Parabolic and linear symmetry descriptions are used to model and extract local features including ridge orientation and reliability, which can be reused in several stages of fingerprint processing. Experimental results on the proposed technique are presented.
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paper_title: Fuzzy feature selection for fingerprint identification

paper_content:

Fingerprint verification systems are expensive and complex, requiring sensing facilities, preprocessing algorithms for image quality enhancement and procedures for ridge and minutiae detection which can be used for classification, verification and recognition. To-date, the main published approaches to fingerprint recognition break down the process of ridge detection into smoothing or early pre-processing, edge detection, thresholding, binarization and subsequently thinning. This whole procedure can be very computationally expensive and hence requires more expensive hardware to meet the response-time requirements. The approach presented in this paper is based on fuzzy logic techniques. This has the advantage of being simple and less expensive.
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paper_title: Fingerprint image: pre- and post-processing

paper_content:

Automatic Fingerprint Identification Systems (AFIS) are widely used for personal identification due to uniqueness of fingerprints. Minutiae-based fingerprint matching techniques are normally used for fingerprint matching. Fingerprint matching results and their accuracy depends on presence of valid minutiae. In this paper, we present a new technique for fingerprint image post-processing. This post-processing is used to eliminate a large number of false extracted minutiae from skeletonised fingerprint images. We propose a windowing post-processing method that takes into account the neighbourhood of each minutia within defined window and check for minutia validation and invalidation. We also present a complete pre-processing system including new segmentation technique that is required to extract region of interest (ROI) accurately from a fingerprint image. The results are confirmed by visual inspections of validated minutiae of the FVC2004 reference fingerprint image database. Experimental results obtained by the proposed approach show efficient reduction of false minutiae.
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paper_title: Minutiae Extraction in Fingerprint Using Gabor Filter Enhancement

paper_content:

Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper the Minutiae Extraction in Fingerprint using Gabor Filter Enhancement (MEGFE) algorithm is proposed for minutiae feature extraction and post processing. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms.
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paper_title: Real-Time Minutiae Extraction In Fingerprint Images

paper_content:

Feature extraction is one of the most important tasks in an automatic fingerprint identification system (AFIS). The characteristics to be extracted in a given fingerprint image can be divided into two main categories: global or high-level features and local or low-level features. Cores and deltas are examples of global features. Their number and location in the fingerprint are used to classify the fingerprint (given a previously defined set of possible classes). Local features (such as endings and/or bifurcations of fingerprint ridges) are used to obtain a unique characterization of the given fingerprint. These local features are commonly named minutiae. The paper begins with a broad description of a proposed method for minutiae extraction. The different modules of the system are subsequently explained. Some examples of the application of the method on real fingerprint images taken from two different databases are discussed.
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paper_title: Fingerprint minutiae matching based on the local and global structures

paper_content:

Proposes a fingerprint minutia matching technique, which matches the fingerprint minutiae by using both the local and global structures of minutiae. The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood. It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching. The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching. The proposed minutiae matching scheme is suitable for an online processing due to its high processing speed. Experimental results show the performance of the proposed technique.
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paper_title: An efficient algorithm for fingerprint preprocessing and feature extraction

paper_content:

Abstract In practice, the placement of finger on the scanner for authentication is not done with the utmost care as when placed during the enrollment and this result in rejections of genuine users. Moreover, user behaviour and envionmental conditions decrease the geniune acceptance rate (GAR) for authentication of fingerprints. To overcome these limitations, an efficient preprocessing algorithm is proposed to achieve good vertical orientation and high ridge curvature area around the core point for fingerprint authentication and analysis. The algorithm is implemented in two stages. The process of obtaining the vertical oriented fingerprint image is carried out in the first step. This is followed by core point detection of a fingerprint. Core point detection is efficiently identified for any type of fingerprints. The developed algorithm is tested using a line based feature extraction algorithm with a large internal database and samples of fingerprint vericication competition (FVC). Only for the poor quality images, broken ridges are identified which results in a difference in minutiae points. With the proposed algorithm 94% of the tested images were oriented vertically and its genuiness is verified by comparing the minutiae details of the oriented and unoriented image of the same subject.
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paper_title: Direct minutiae extraction from gray-level fingerprint image by relationship examination

paper_content:

A new method to extract the minutiae from a gray-level fingerprint image is presented in this paper. The proposed method determines the minutiae by examining the relationship between the ridges and furrows. The minutiae change the relationship between the ridges and furrows, so the minutiae can be detected by finding the relationship change. By following the ridges and furrows along the local direction, the relationship change can be extracted. Experimental results demonstrate that the method developed based on the new approach is insensitive to noise and hence extracts minutiae better.
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paper_title: Using Linear Symmetry Features as a Pre-processing Step for Fingerprint Images

paper_content:

This paper presents the idea to use linear symmetry properties as a feature based pre-processing step for fingerprint images. These features contain structural information of the local patterns. The linear symmetry can be computed by using separable spatial filtering and therefore has the potential to be a fast pre-processing step. Our results indicate that minutiae can be located as well as can be assigned a certain class type. The type of minutiae matching in combination with geometrical matching increases the matching efficiency as compared to the pure geometrical matching.
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paper_title: Fingerprint classification using a feedback-based line detector

paper_content:

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: arch, left loop, right loop, whorl, and tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.
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paper_title: Image Compression Algorithms for Fingerprint System

paper_content:

Fingerprint-which have been used for about 100 years are the oldest biometric signs of identity. Humans have used fingerprints for personal identification for centuries and the validity of fingerprint identification has been well established. In fact, fingerprint technology is so common in Human Identification that it has almost become the synonym of biometrics. Fingerprints are believed to be unique across individuals and across fingers of same individual. Even identical twins having similar DNA, are believed to have different fingerprints. The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies. A major approach for fingerprint recognition today is to extract minutiae from fingerprint images and to perform fingerprint matching based on the number of corresponding minutiae pairings. One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. Addressing this problem this paper propose some extra features that can be used to strengthen the present approaches followed in developing Fingerprint recognition system. To increase security and accuracy we can use Infrared technique and technique to assign a score value to each of extracted minutiae.
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paper_title: Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge

paper_content:

Abstract This paper presents a minutiae detection procedure based on adaptive tracing the gray-level ridge of the fingerprint image with piecewise linear lines of different length. The original fingerprint image is smoothed with an adaptive-oriented smoothing filter only at some selected points. This will greatly reduce the computational time. Each ridge in the skeleton is labeled with a number so that each detected minutia is associated with one or two ridge numbers, which is useful for post processing. We objectively assess the performance of this approach by using two large fingerprint databases.
---
paper_title: Detecting fingerprint minutiae by run length encoding scheme

paper_content:

Many approaches to minutiae extraction have already been proposed for automatic fingerprint matching, and most transform fingerprint images into binary images through state-of-the-art algorithms and submit the binary image to a thinning process. However, this paper proposes an original technique for extracting minutiae based on representing the ridge structure of a fingerprint image as a run length code (RLC). The essential idea is to detect minutiae by searching for the termination points or bifurcation points of ridges in the RLC, rather than in a fingerprint image. Experimental results and a comparative analysis show that the proposed method is fairly reliable and faster than a conventional thinning-based method.
---
paper_title: Run-based algorithms for binary image analysis and processing

paper_content:

In this paper we suggest a variant of a binary image representation based on run length encoding. This variant allows one to build a "graph representation" for a number of computing tasks like component labeling, computations of Euler number, diameter and convex hull, and the detection of local extrema and multiple points. Finally, a running application in the raster-to-vector conversion of digital maps is provide.
---
paper_title: An enhanced Gabor filter-based segmentation algorithm for fingerprint recognition systems

paper_content:

An important step in fingerprint recognition is the segmentation of the region of interest. In this paper, we present an enhanced approach for fingerprint segmentation based on the response of eight oriented Gabor filters. The performance of the algorithm has been evaluated in terms of decision error trade-off curves of an overall verification system. Experimental results demonstrate the robustness of the proposed method.
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paper_title: Segmentation of Fingerprint Images Using Linear Classifier

paper_content:

An algorithm for the segmentation of fingerprints and a criterion for evaluating the block feature are presented. The segmentation uses three block features: the block clusters degree, the block mean information, and the block variance. An optimal linear classifier has been trained for the classification per block and the criteria of minimal number of misclassified samples are used. Morphology has been applied as post processing to reduce the number of classification errors. The algorithm is tested on FVC2002 database, only 2.45% of the blocks are misclassified, while the postprocessing further reduces this ratio. Experiments have shown that the proposed segmentation method performs very well in rejecting false fingerprint features from the noisy background.
---
paper_title: Pipelined minutiae extraction from fingerprint images

paper_content:

Fingerprint identification is an important biometric technique for personal identification. Most of the automatic systems of fingerprint recognition are based on matching of fingerprint minutiae. In this paper, we described a method that reduces the complexity of computations and extract the minutiae from binary image with appropriate speed. In this paper a new hardware scheme is presented which is based on pipelined architecture in order to real-time minutiae extraction. Simulations and implementation result shows the operation and accuracy of suggested method.
---
paper_title: Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition

paper_content:

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs.
---
paper_title: Fingerprint local analysis for high-performance minutiae extraction

paper_content:

The paper presents a novel approach to identify the minutiae present in a fingerprint image based on the analysis of the local properties. The typical patterns of minutiae called ridge termination and bifurcation are identified by studying the intensity along squared paths in the image. The presented algorithm works both on grey-level image and binarized image and, despite its simplicity, it achieves good accuracy and it can be a good candidate to be implemented in hardware or executed on simple hardware architectures, for example in biometric systems embedded in portable applications such as cellular phones and smart cards.
---
paper_title: A systematic method for fingerprint ridge orientation estimation and image segmentation

paper_content:

This paper proposes a scheme for systematically estimating fingerprint ridge orientation and segmenting fingerprint image by means of evaluating the correctness of the ridge orientation based on neural network. The neural network is used to learn the correctness of the estimated orientation by gradient-based method. The trained network is able to distinguish correct and incorrect ridge orientations, and as a consequence, the falsely estimated ridge orientation of a local image block can be corrected using the around blocks of which orientations are correctly estimated. A coarse segmentation can also be done based on the trained neural network by taking the blocks of correctly estimated orientation as foreground and the blocks of incorrectly estimated orientation as background. Besides, following the steps of estimating ridge orientation correctness, a secondary segmentation method is proposed to segment the remaining ridges which are the afterimage of the previously scanned fingers. The proposed scheme serves for minutiae detection and is compared with VeriFinger 4.2 published by Neurotechnologija Ltd. in 2004, and the comparison shows that the proposed scheme leads to an improved accuracy of minutiae detection.
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paper_title: An Optimized Approach for Fingerprint Binarization

paper_content:

This paper presents a novel approach for fingerprint binarization. The motivation is to simulate the human exercise that distinguishes ridges from valleys in uniform areas. An algorithm for performing such a region generation on fingerprint images is presented, and a data structure for saving generated regions is developed. Thresholding and binarization is then performed individually on each generated region using traditional binarization methods. The presented approach is particularly useful for processing fingerprint images captured with non-uniform inking or light. Experimental results show that this algorithm can achieve promising results compared with traditional binarization approaches.
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paper_title: Detecting fingerprint minutiae by run length encoding scheme

paper_content:

Many approaches to minutiae extraction have already been proposed for automatic fingerprint matching, and most transform fingerprint images into binary images through state-of-the-art algorithms and submit the binary image to a thinning process. However, this paper proposes an original technique for extracting minutiae based on representing the ridge structure of a fingerprint image as a run length code (RLC). The essential idea is to detect minutiae by searching for the termination points or bifurcation points of ridges in the RLC, rather than in a fingerprint image. Experimental results and a comparative analysis show that the proposed method is fairly reliable and faster than a conventional thinning-based method.
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paper_title: A rotation invariant rule-based thinning algorithm for character recognition

paper_content:

This paper presents a novel rule-based system for thinning. The unique feature that distinguishes our thinning system is that it thins symbols to their central lines. This means that the shape of the symbol is preserved. It also means that the method is rotation invariant. The system has 20 rules in its inference engine. These rules are applied simultaneously to each pixel in the image. Therefore, the system has the advantages of symmetrical thinning and speed. The results show that the system is very efficient in preserving the topology of symbols and letters written in any language.
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paper_title: Rotation invariant thinning algorithm to detect ridge bifurcations for fingerprint identification

paper_content:

In this paper we have modified the thinning algorithm proposed by Ahmed and Ward (2002). The unique feature that distinguishes the algorithm is its ability to thin any symbol or fingerprint to its central line taking care that the shape of the symbol is preserved while being rotation invariant. Our modified algorithm also incorporates a process to thin zigzag diagonal lines having a width of two pixels which was not considered in "a rotation invariant rule-based thinning algorithm for character recognition" (Ahmed and Ward, 2002). The algorithm is iterative and makes use of parallel processing to speed up execution. The system has 21 rules in its inference engine which are applied simultaneously to every pixel in each iteration. The algorithm is implemented for thinning fingerprints, fonts and symbols to a single pixel width. We also introduce a 24 rule based mask for detection of ridge bifurcations, which can be helpful for recognition/authentication of fingerprints as a biometric
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paper_title: Multiscale approach for thinning ridges of fingerprint

paper_content:

This paper presents a robust multiscale method to create thinned ridge map of fingerprint for automatic recognition by employing an elaborately designed wavelet function. Properties of the new wavelet function are substantially investigated. Some desirable characteristics of the local minimum produced by wavelet transform show that they are suitable to describe skeleton of ribbon-shape objects. A multiscale thinning algorithm based on the modulus minima of wavelet transform is proposed. The proposed algorithm is able to improve the skeleton representation of the ridge of fingerprint without side-effects and limitations of previous methods. Thinned ridge map helps to facilitate minutiae extraction for matching. Experiments validated effectiveness and efficiency of the proposed method.
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paper_title: Fingerprint minutiae extraction from skeletonized binary images

paper_content:

Abstract Fingerprint comparison is usually based on minutiae matching. The minutiae considered in automatic identification systems are normally ridge bifurcations and terminations. In this paper we present a set of algorithms for the extraction of fingerprint minutiae from skeletonized binary images. The goal of the present work is the extraction of the real 40–60 minutiae of a fingerprint image from the 2000–3000 contained in typical skeletonized and binarized images. Besides classical methodologies for minutiae filtering, a new approach is proposed for bridge cleaning based on ridge positions instead of classical methods based on directional maps. Finally, two novel criteria and related algorithms are introduced for validating the endpoints and bifurcations. Statistical analysis of the results obtained by the proposed approach shows efficient reduction of spurious minutiae. The use of the fingerprint minutiae extraction algorithms has also been considered in a fingerprint identification system in terms of timing and false reject or acceptance rates. The presented minutiae extraction algorithm performs correctly in dirty areas and on the background as well, making computationally expensive segmentation algorithms unnecessary. The results are confirmed by visual inspections of validated minutiae of the NIST sdb 4 reference fingerprint image database.
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paper_title: Fingerprint image postprocessing: A combined statistical and structural approach

paper_content:

Abstract For complex patterns such as fingerprints, it is difficult to describe the dactylotype by using a set of feature vectors, as well as to construct and parse all of the individual fingerprint pattern with a syntactical approach. In this paper, which is different from the conventional methods, we present an approach in which the statistical and structural approaches are combined to represent the fingerprint false minutia patterns. Based on the proposed approach, a fingerprint image postprocessing algorithm is developed to eliminate the false minutiae in fingerprint images. Because a global minutia relation is considered on the whole image, experimental results demonstrate the advantages of the combined approach.
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paper_title: Learning Fingerprint Minutiae Location and Type

paper_content:

For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by ∼1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by ∼3.2% on our fingerprint database.
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paper_title: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

paper_content:

In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.
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paper_title: An identity-authentication system using fingerprints

paper_content:

Fingerprint verification is an important biometric technique for personal identification. We describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction algorithm that is faster and more accurate than our earlier algorithm (1995). An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA I workstation (it is expected to run as fast or faster on a 200 HMz Pentium).
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paper_title: A novel method for fingerprint feature extraction

paper_content:

Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques [1] based on high-resolution fingerprints images of the individual. Fingerprints have been used in forensic as well as commercial applications for identification as well as verification. The fingerprint surface is made up of a system of ridges and valleys. The steps for Fingerprint recognition include image acquisition, preprocessing, feature extraction and matching [2]. In the present work, a new fingerprint feature detection algorithm has been proposed. It has been found that presence of noise in fingerprint images leads to spurious minutiae. To overcome this problem, feature extraction has been done which efficiently determine the minutiae points in fingerprint [1]. The proposed method can be used in matching the template for finding bifurcation and termination. The new smoothing algorithm is proposed for the detection of the features of fingerprints. A method has been introduced for finding ridges in the fingerprint image with the help of eight different masks. It is a process of making a binary image of ridges from the grayscale fingerprint image. The experimental results showed the accuracy of the algorithm in terms of genuine acceptance rate, false rejection rate, false acceptance rate.
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paper_title: Fingerprint recognition using neural network

paper_content:

The authors describe a neural network based approach for automated fingerprint recognition. Minutiae are extracted from the fingerprint image via a multilayer perceptron (MLP) classifier with one hidden layer. The backpropagation learning technique is used for its training. Selected features are represented in a special way such that they are simultaneously invariant under shift, rotation and scaling. Simulation results are obtained with good detection ratio and low failure rate. The proposed method is found to be reliable for a system with a small set of fingerprint data. >
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paper_title: A Novel Approach for Fingerprint Matching Using Minutiae

paper_content:

Fingerprint recognition is one of the most important challenging tasks of the biometrics identification due to the fact that they are easy to obtain and most importantly they are reliable. A number of pattern recognition methods have been used to perform fingerprint matching. Among such methods fingerprint matching based on minutia features is one of the researched problems. So we proposed effective fingerprint matching algorithm based on feature extraction. Also we have presented a novel fingerprint recognition technique for minutia extraction and a minutia matching. For minutia marking we considered one special false minutiae removal method and for minutiae matching we considered alignment based method. Experimental results show that this method achieves a much better matching performance.
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paper_title: Novel Approaches for Minutiae Verification in Fingerprint Images

paper_content:

A majority of the existing fingerprint recognition algorithms are based on matching minutia features. Therefore, minutiae extraction is one of the critical steps in fingerprint verification algorithms. Poor quality fingerprint images lead to missing and spurious minutiae that degrade the performance of the matching system. We propose two new techniques for minutiae verification based on non-trivial gray level features. The proposed features intuitively represent the structural properties of the minutiae neighborhood leading to better classification. We use directionally selective steerable wedge filters to differentiate between minutiae and non-minutiae neighborhoods. We also propose a second technique based on Gabor expansion that results in even better discrimination. We present an objective evaluation of both the algorithms.
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paper_title: An algorithm for fingerprint image postprocessing

paper_content:

Most of the current fingerprint identification and verification systems performs fingerprint matching based on different attributes of the minutia details present in fingerprints. The minutiae (i.e. ridge endings and ridge bifurcations) are usually detected in the thinned binary image of the fingerprint. Due to the presence of noise as well as the use of different preprocessing stages the thinned binary image contains a large number of false minutiae which may highly decrease the matching performance of the system. A new algorithm of fingerprint image postprocessing is proposed. The algorithm operates onto the thinned binary image of the fingerprint in order to eliminate the false minutiae. The proposed algorithm is able to detect and cancel the minutiae associated with most of the false minutia structures which may be encountered in the thinned fingerprint image.
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paper_title: Fingerprint image: pre- and post-processing

paper_content:

Automatic Fingerprint Identification Systems (AFIS) are widely used for personal identification due to uniqueness of fingerprints. Minutiae-based fingerprint matching techniques are normally used for fingerprint matching. Fingerprint matching results and their accuracy depends on presence of valid minutiae. In this paper, we present a new technique for fingerprint image post-processing. This post-processing is used to eliminate a large number of false extracted minutiae from skeletonised fingerprint images. We propose a windowing post-processing method that takes into account the neighbourhood of each minutia within defined window and check for minutia validation and invalidation. We also present a complete pre-processing system including new segmentation technique that is required to extract region of interest (ROI) accurately from a fingerprint image. The results are confirmed by visual inspections of validated minutiae of the FVC2004 reference fingerprint image database. Experimental results obtained by the proposed approach show efficient reduction of false minutiae.
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paper_title: Minutiae Extraction in Fingerprint Using Gabor Filter Enhancement

paper_content:

Human fingerprints are reliable characteristics for personnel identification as it is unique and persistence. A fingerprint pattern consists of ridges, valleys and minutiae. In this paper the Minutiae Extraction in Fingerprint using Gabor Filter Enhancement (MEGFE) algorithm is proposed for minutiae feature extraction and post processing. A global feature extraction and fingerprints enhancement are based on Hong enhancement method which is simultaneously able to extract local ridge orientation and ridge frequency. It is observed that the Sensitivity and Specificity values are better compared to the existing algorithms.
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paper_title: Real-Time Minutiae Extraction In Fingerprint Images

paper_content:

Feature extraction is one of the most important tasks in an automatic fingerprint identification system (AFIS). The characteristics to be extracted in a given fingerprint image can be divided into two main categories: global or high-level features and local or low-level features. Cores and deltas are examples of global features. Their number and location in the fingerprint are used to classify the fingerprint (given a previously defined set of possible classes). Local features (such as endings and/or bifurcations of fingerprint ridges) are used to obtain a unique characterization of the given fingerprint. These local features are commonly named minutiae. The paper begins with a broad description of a proposed method for minutiae extraction. The different modules of the system are subsequently explained. Some examples of the application of the method on real fingerprint images taken from two different databases are discussed.
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paper_title: Fingerprint minutiae matching based on the local and global structures

paper_content:

Proposes a fingerprint minutia matching technique, which matches the fingerprint minutiae by using both the local and global structures of minutiae. The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood. It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching. The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching. The proposed minutiae matching scheme is suitable for an online processing due to its high processing speed. Experimental results show the performance of the proposed technique.
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paper_title: An efficient algorithm for fingerprint preprocessing and feature extraction

paper_content:

Abstract In practice, the placement of finger on the scanner for authentication is not done with the utmost care as when placed during the enrollment and this result in rejections of genuine users. Moreover, user behaviour and envionmental conditions decrease the geniune acceptance rate (GAR) for authentication of fingerprints. To overcome these limitations, an efficient preprocessing algorithm is proposed to achieve good vertical orientation and high ridge curvature area around the core point for fingerprint authentication and analysis. The algorithm is implemented in two stages. The process of obtaining the vertical oriented fingerprint image is carried out in the first step. This is followed by core point detection of a fingerprint. Core point detection is efficiently identified for any type of fingerprints. The developed algorithm is tested using a line based feature extraction algorithm with a large internal database and samples of fingerprint vericication competition (FVC). Only for the poor quality images, broken ridges are identified which results in a difference in minutiae points. With the proposed algorithm 94% of the tested images were oriented vertically and its genuiness is verified by comparing the minutiae details of the oriented and unoriented image of the same subject.
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paper_title: Fingerprint classification using a feedback-based line detector

paper_content:

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: arch, left loop, right loop, whorl, and tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.
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paper_title: Image Compression Algorithms for Fingerprint System

paper_content:

Fingerprint-which have been used for about 100 years are the oldest biometric signs of identity. Humans have used fingerprints for personal identification for centuries and the validity of fingerprint identification has been well established. In fact, fingerprint technology is so common in Human Identification that it has almost become the synonym of biometrics. Fingerprints are believed to be unique across individuals and across fingers of same individual. Even identical twins having similar DNA, are believed to have different fingerprints. The analysis of fingerprints for matching purposes generally requires the comparison of several features of the print pattern. These include patterns, which are aggregate characteristics of ridges, and minutia points, which are unique features found within the patterns. is also necessary to know the structure and properties of human skin in order to successfully employ some of the imaging technologies. A major approach for fingerprint recognition today is to extract minutiae from fingerprint images and to perform fingerprint matching based on the number of corresponding minutiae pairings. One of the most difficult problems in fingerprint recognition has been that the recognition performance is significantly influenced by fingertip surface condition, which may vary depending on environmental or personal causes. Addressing this problem this paper propose some extra features that can be used to strengthen the present approaches followed in developing Fingerprint recognition system. To increase security and accuracy we can use Infrared technique and technique to assign a score value to each of extracted minutiae.
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paper_title: Neural network based minutiae filtering in fingerprints

paper_content:

Minutiae correspond essentially to the terminations and bifurcations of fingerprint patterns. Since the quality of fingerprint images is often low, automatic minutiae detection is a very difficult task and the extraction algorithms produce a large number of false alarms. We present an approach to minutiae filtering based on a neural network. The minutiae neighborhoods extracted by the algorithm presented by us (1997) are normalized with respect to rotation and scale, and their dimensionality is reduced via a KL transform. A neural classifier, whose topology has been designed to exploit the minutiae duality, is employed to perform the neighborhoods classification. The filtering proposed, as confirmed by simulations, allows a significant improvement in the overall performance to be achieved.
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paper_title: A novel method of fingerprint minutiae extraction based on Gabor phase

paper_content:

In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.
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paper_title: Local Feature Extraction in Fingerprints by Complex Filtering

paper_content:

A set of local feature descriptors for fingerprints is proposed. Minutia points are detected in a novel way by complex filtering of the structure tensor, not only revealing their position but also their direction. Parabolic and linear symmetry descriptions are used to model and extract local features including ridge orientation and reliability, which can be reused in several stages of fingerprint processing. Experimental results on the proposed technique are presented.
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paper_title: Direct minutiae extraction from gray-level fingerprint image by relationship examination

paper_content:

A new method to extract the minutiae from a gray-level fingerprint image is presented in this paper. The proposed method determines the minutiae by examining the relationship between the ridges and furrows. The minutiae change the relationship between the ridges and furrows, so the minutiae can be detected by finding the relationship change. By following the ridges and furrows along the local direction, the relationship change can be extracted. Experimental results demonstrate that the method developed based on the new approach is insensitive to noise and hence extracts minutiae better.
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paper_title: Using Linear Symmetry Features as a Pre-processing Step for Fingerprint Images

paper_content:

This paper presents the idea to use linear symmetry properties as a feature based pre-processing step for fingerprint images. These features contain structural information of the local patterns. The linear symmetry can be computed by using separable spatial filtering and therefore has the potential to be a fast pre-processing step. Our results indicate that minutiae can be located as well as can be assigned a certain class type. The type of minutiae matching in combination with geometrical matching increases the matching efficiency as compared to the pure geometrical matching.
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paper_title: Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge

paper_content:

Abstract This paper presents a minutiae detection procedure based on adaptive tracing the gray-level ridge of the fingerprint image with piecewise linear lines of different length. The original fingerprint image is smoothed with an adaptive-oriented smoothing filter only at some selected points. This will greatly reduce the computational time. Each ridge in the skeleton is labeled with a number so that each detected minutia is associated with one or two ridge numbers, which is useful for post processing. We objectively assess the performance of this approach by using two large fingerprint databases.
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paper_title: Hybrid fuzzy logic and neural network model for fingerprint minutiae extraction

paper_content:

This paper presents the research into the use of fuzzy-neuro technology in automated finger print recognition for the extraction of fingerprint features, known as minutiae. The work presented here is an addendum to work carried out earlier by Sagar et al. (1995).
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paper_title: Fuzzy feature selection for fingerprint identification

paper_content:

Fingerprint verification systems are expensive and complex, requiring sensing facilities, preprocessing algorithms for image quality enhancement and procedures for ridge and minutiae detection which can be used for classification, verification and recognition. To-date, the main published approaches to fingerprint recognition break down the process of ridge detection into smoothing or early pre-processing, edge detection, thresholding, binarization and subsequently thinning. This whole procedure can be very computationally expensive and hence requires more expensive hardware to meet the response-time requirements. The approach presented in this paper is based on fuzzy logic techniques. This has the advantage of being simple and less expensive.
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Title: Minutiae Extraction from Fingerprint Images - a Review

Section 1: Introduction
Description 1: Provide an overview of biometrics, specifically focusing on fingerprint identification and the importance of minutiae points.

Section 2: Fingerprint Features
Description 2: Discuss the classification of fingerprint features into three levels and the details of global and local ridge patterns.

Section 3: Global Ridge Pattern
Description 3: Explain the global ridge pattern including singular points, ridge orientation map, and ridge frequency map.

Section 4: Local Ridge Pattern
Description 4: Describe the local ridge pattern, focusing on minutiae types and their significance in fingerprint recognition.

Section 5: What is Fingerprint Recognition?
Description 5: Define fingerprint recognition and differentiate between its sub-domains: fingerprint enrollment, verification, and identification.

Section 6: Techniques for Fingerprint Matching
Description 6: Outline various fingerprint matching techniques such as minutiae-based, pattern matching, correlation-based, and image-based techniques.

Section 7: Minutiae Extraction
Description 7: Discuss the importance of accurate minutiae extraction for automatic fingerprint identification systems and provide a classification of minutiae extraction methods.

Section 8: Minutiae detection from binarized fingerprints
Description 8: Explain methods for detecting minutiae from binarized fingerprints, including techniques for unthinned and thinned binarized images.

Section 9: Minutiae Extraction from Gray-Level images
Description 9: Explore methods for directly extracting minutiae from gray-level fingerprint images, highlighting the advantages and techniques involved.

Section 10: Conclusions
Description 10: Summarize the importance of image quality in fingerprint authentication systems and the various methods reviewed for minutiae extraction, suggesting areas for further research.