1 Image Compression in Face Recognition-a Literature Survey
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paper_title: The JPEG still picture compression standard

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For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG’s proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for “lossy’’ compression, and a predictive method for “lossless’’ compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method.
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paper_title: Effects of JPEG and JPEG2000 Compression on Face Recognition

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In this paper we analyse the effects that JPEG and JPEG2000 compression have on subspace appearance-based face recognition algorithms. This is the first comprehensive study of standard JPEG2000 compression effects on face recognition, as well as an extension of existing experiments for JPEG compression. A wide range of bitrates (compression ratios) was used on probe images and results are reported for 12 different subspace face recognition algorithms. Effects of image compression on recognition performance are of interest in applications where image storage space and image transmission time are of critical importance. It will be shown that not only that compression does not deteriorate performance but it, in some cases, even improves it slightly. Some unexpected effects will be presented (like the ability of JPEG2000 to capture the information essential for recognizing changes caused by images taken later in time) and lines of further research suggested.
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paper_title: Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

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Face recognition on mobile devices, such as personal digital assistants and cell phones, is a big challenge owing to the limited computational resources available to run verifications on the devices themselves. One approach is to transmit the captured face images by use of the cell-phone connection and to run the verification on a remote station. However, owing to limitations in communication bandwidth, it may be necessary to transmit a compressed version of the image. We propose using the image compression standard JPEG2000, which is a wavelet-based compression engine used to compress the face images to low bit rates suitable for transmission over low-bandwidth communication channels. At the receiver end, the face images are reconstructed with a JPEG2000 decoder and are fed into the verification engine. We explore how advanced correlation filters, such as the minimum average correlation energy filter [ ::: Appl. Opt.26, ::: 3633 ( ::: 1987)] and its variants, perform by using face images captured under different illumination conditions and encoded with different bit rates under the JPEG2000 wavelet-encoding standard. We evaluate the performance of these filters by using illumination variations from the Carnegie Mellon University’s Pose, Illumination, and Expression (PIE) face database. We also demonstrate the tolerance of these filters to noisy versions of images with illumination variations.
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paper_title: A Critical Evaluation of Image and Video Indexing Techniques in the Compressed Domain

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Abstract Image and video indexing techniques are crucial in multimedia applications. A number of indexing techniques that operate in the pixel domain have been reported in the literature. The advent of compression standards has led to the proliferation of indexing techniques in the compressed domain. In this paper, we present a critical review of the compressed domain indexing techniques proposed in the literature. These include transform domain techniques using Fourier transform, cosine transform, Karhunen–Loeve transform, Subbands and wavelets, and spatial domain techniques using vector quantization and fractals. In addition, temporal indexing techniques using motion vectors are also discussed.
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paper_title: Face detection in the compressed domain

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Face detection is important in many algorithms in the areas of machine object recognition and pattern recognition. The kaleidoscope of applications for face detection extends across automatic image and home video content annotation, face-image stabilisation and face recognition systems. By using information derived from colour, luminosity and frequency the face detection algorithm proposed in this paper aims to determine the location of multiple frontal and non-frontal faces in compressed MPEG-1, MPEG-2 video or JPEG image content. The described algorithm requires only low computational resources on CE devices and offers at one and the same time extremely high detection rates.
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paper_title: Object Recognition in Compressed Imagery

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Image-based applications can save time and space by operating on compressed data. The problem is that most mid- and high-level image operations, such as object recognition, are formulated as sequences of operations in the image domain. Such methods need direct access to pixel information as a starting point, but the pixel information in a compressed image stream is not immediately accessible. In this paper we show how to perform object recognition directly on compressed images (JPEG) and index frames from video streams (MPEG I-frames) without recovering explicit pixel information. The approach uses eigenvectors constructed from compressed image data. Our performance results show that a five-fold speedup can be gained by using compressed data.
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paper_title: Application of the DCT Energy Histogram for Face Recognition

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In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.
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paper_title: Image Indexing in DCT Domain

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With the rapid growing of multimedia technology, more and more multimedia content are disseminated in the network or stored in the database. Image data is one of the multimedia types to be seen or accessed for the users in the Internet or from the database. Searching the related images by the querying image content is helpful to the management of image database. Most of the images are joint photographic experts group (JPEG) file format. Full decompression of JPEG file to spatial domain to extract the features for indexing takes time. Therefore, the research of JPEG file format indexing technique is worthwhile because it does not need the extra time to fulfill inverse discrete cosine transform (IDCT). Essentially, the proposed method is a content-based image retrieval (CBIR) system that can retrieval the images from the image database. In order to increase the efficiency of processing time, the proposed method fulfills partial decoding to JPEG file directly instead of full decoding and the feature vector extracted from the partial decoding can ensure the precise indexing as well. The system can output ranked images according to the similarity values in short time. In addition, the system has the property of robustness to rotation, scaling, translation, darkening, lightening, cropping, noise corruption, etc. Simulation results demonstrate that the performance of the proposed method outperforms the other existing DCT-based image indexing skills both from the aspects of robustness and processing burden
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paper_title: Pattern recognition in compressed DCT domain

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Images and video are currently predominantly handled in compressed form. Block-based compression standards are by far the most widespread. It is thus important to devise information processing methods operating directly in compressed domain. In this paper we investigate this possibility on the example of simple face information processing method based on the DCT (discrete cosine transform) blocks. We use patterns of quantized 4x4 DCT blocks for representing local picture information. These patterns at different quantization levels provide very flexible representation of picture information. We represent global information in pictures by histograms of quantized DCT pattern distributions. The approach is tested on database of face images and it is shown that despite its simplicity provides good results in the face recognition problem.
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paper_title: Exploiting Image Indexing Techniques in DCT domain

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This paper is concerned with the indexing and retrieval of images based on features extracted directly from the JPEG discrete cosine transform (DCT) domain. We examine possible ways of manipulating DCT coe$cients by standard image analysis approaches to describe image shape, texture, and color. Through the Mandala transformation, our approach groups a subset of DCT coe$cients to form ten blocks. Each block represents a particular frequency content of the original image. Two blocks are used to model rough object shape; nine blocks to describe subband properties; and one block to compute color distribution. As a result, the amount of data used for processing and analysis is signi"cantly reduced. This can lead to simple yet e$cient ways of indexing and retrieval in a large-scale image database. Experimental results show that our proposed approach o!ers superior indexing speed without signi"cantly sacri"cing the retrieval accuracy. 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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paper_title: Image retrieval based on energy histograms of the low frequency DCT coefficients

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With the increasing popularity of the use of compressed images, an intuitive approach for lowering computational complexity towards a practically efficient image retrieval system is to propose a scheme that is able to perform retrieval computation directly in the compressed domain. In this paper, we investigate the use of energy histograms of the low frequency DCT coefficients as features for the retrieval of DCT compressed images. We propose a feature set that is able to identify similarities on changes of image-representation due to several lossless DCT transformations. We then use the features to construct an image retrieval system based on the real-time image retrieval model. We observe that the proposed features are sufficient for performing high level retrieval on medium size image databases. And by introducing transpositional symmetry, the features can be brought to accommodate several lossless DCT transformations such as horizontal and vertical mirroring, rotating, transposing, and transversing.
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paper_title: Encoding and recognition of faces based on the human visual model and DCT

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For encoding and recognizing human faces in monochrome images, we propose a new method based on a combination of the discrete cosine transform (DCT), principal component analysis (PCA), and the characteristics of the Human Visual System. The novel aspect of the proposed non-Bayesian, approach is that, in the course of the recognition of face images, we also achieve image compression (in the form of encoding). With the help of examples, we demonstrate the superiority and advantages of the new method in comparison with the results found in the literature.
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paper_title: Pattern retrieval using optimized compression transform

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Images and video are currently predominantly handled in compressed form. Block-based compression standards are by far the most widespread. It is thus important to devise information processing methods operating directly in compressed domain. In this paper we investigate this possibility on the example of simple face information processing method based on the H.264 AC Transformed blocks. We use patterns of quantized 4x4 transformed blocks for representing local picture information. These patterns at different quantization levels provide very flexible representation of picture information. By combining both the AC and DC information, we represent global information in pictures by histograms of quantized block pattern distributions. The approach is tested on FERET database of face images and it is shown that despite its simplicity provides good results in the face recognition problem.
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paper_title: Exploiting the JPEG compression scheme for image retrieval

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We address the problem of retrieving images from a large database using an image as a query. The method is specifically aimed at databases that store images in JPEG format, and works in the compressed domain to create index keys. A key is generated for each image in the database and is matched with the key generated for the query image. The keys are independent of the size of the image. Images that have similar keys are assumed to be similar, but there is no semantic meaning to the similarity.
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paper_title: Direct content access and extraction from JPEG compressed images

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Abstract In this paper, we propose a novel design of content access and extraction algorithm for compressed image browsing and indexing, which is critical for all visual information systems. By analyzing the relationship between DCT coefficients of one block of 8×8 pixels and its four sub-blocks of 4×4 pixels, the proposed algorithm extract an approximated image with smaller size for indexing and content browsing without incurring full decompression. While the computing cost is significantly lower than full decompression, the approximated image also reserves the content features, which are sufficient for indexing and browsing as evidenced by our extensive experiments.
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paper_title: Face Recognition Using the Discrete Cosine Transform

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An accurate and robust face recognition system was developed and tested. This system exploits the feature extraction capabilities of the discrete cosine transform (DCT) and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. The method was tested on a variety of available face databases, including one collected at McGill University. The system was shown to perform very well when compared to other approaches.
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paper_title: Object Recognition in Compressed Imagery

paper_content:

Image-based applications can save time and space by operating on compressed data. The problem is that most mid- and high-level image operations, such as object recognition, are formulated as sequences of operations in the image domain. Such methods need direct access to pixel information as a starting point, but the pixel information in a compressed image stream is not immediately accessible. In this paper we show how to perform object recognition directly on compressed images (JPEG) and index frames from video streams (MPEG I-frames) without recovering explicit pixel information. The approach uses eigenvectors constructed from compressed image data. Our performance results show that a five-fold speedup can be gained by using compressed data.
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paper_title: When eigenfaces are combined with wavelets

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This paper presents a novel and interesting combination of wavelet techniques and eigenfaces to extract features for face recognition. Eigenfaces reduce the dimensions of face vectors while wavelets reveal information that is unavailable in the original image. Extensive experiments have been conducted to test the new approach on the ORL face database, using a radial basis function neural network classifier. The results of the experiments are encouraging and the new approach is a step forward in face recognition.
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paper_title: Recognition in the wavelet domain: a survey

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The use of wavelets has grown enormously since their original inception in the mid-1980s. Since the wavelet data repre- sentation combines spatial, frequency, and scale information in a sparse data representation, they are very useful in a number of image processing applications. This paper discusses current work in applying wavelets to object and pattern recognition. Feature extrac- tion methods and search algorithms for matching images are dis- cussed. Some important issues are the search for invariant repre- sentations, similarities between existing applications and the human visual system, and the derivation of wavelets that match specific targets. Results from several existing systems and areas for future research are presented. © 2001 SPIE and IS&T.
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paper_title: Face recognition by applying wavelet subband representation and kernel associative memory

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In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.
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paper_title: Wavelet packet analysis for face recognition

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Abstract A novel method for recognition of frontal views of human faces under roughly constant illumination is presented. The proposed scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, we build compact and meaningful feature vectors, using simple statistical measures. Then, we show how an efficient and reliable probabilistic metric derived from the Bhattacharrya distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method.
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paper_title: Wavelet-based texture features can be extracted efficiently from compressed-domain for JPEG2000 coded images

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The contribution of this paper is the development of a fast, subband-based JPEG2000 image indexing system in the compressed domain which achieves high memory efficiency. This is the extended work on a previously block-based indexing system. The feature extracted is the variance of each wavelet subband in the compressed domain with the emphasis that subbands are not buffered to maintain memory efficiency. Retrieval performance on VisTex image database indexing has shown the effectiveness and speed up of execution of the proposed features.
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paper_title: Discriminant waveletfaces and nearest feature classifiers for face recognition

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Feature extraction, discriminant analysis, and classification rules are three crucial issues for face recognition. We present hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract the waveletface. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decisions in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.
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Title: Image Compression in Face Recognition-a Literature Survey

Section 1: Introduction
Description 1: Introduce the topic of image compression in face recognition, discuss its relevance, and outline the structure of the survey.

Section 2: Spatial (pixel) domain
Description 2: Overview of research on the impact of degraded image quality due to compression on face recognition accuracy in the spatial (pixel) domain, focusing on JPEG and JPEG2000.

Section 3: JPEG2000
Description 3: Discussion of research on face recognition using images compressed with the JPEG2000 standard, including specific studies and their findings.

Section 4: Analysis
Description 4: Analysis of the reviewed studies, drawing conclusions about the impact of image compression on face recognition and identifying common experimental setups and their implications.

Section 5: Transform (compressed) domain
Description 5: Exploration of research on face recognition performed directly in the compressed (transform) domain using DCT and DWT coefficients.

Section 6: JPEG (DCT coefficients)
Description 6: Review of studies that utilize JPEG DCT coefficients for face recognition, detailing methodologies and findings.

Section 7: JPEG2000 (DWT coefficients)
Description 7: Review of research on using JPEG2000 DWT coefficients in face recognition, including an examination of wavelet-based recognition systems.

Section 8: Conclusions
Description 8: Summarize the key findings from the literature survey, discuss the implications of compression on face recognition accuracy, and suggest areas for future research.