An Overview of Face Liveness Detection
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paper_title: Handbook of Biometrics

paper_content:

Biometrics is a rapidly evolving field with applications ranging from accessing ones computer to gaining entry into a country. The deployment of large-scale biometric systems in both commercial and government applications has increased public awareness of this technology. Recent years have seen significant growth in biometric research resulting in the development of innovative sensors, new algorithms, enhanced test methodologies and novel applications. This book addresses this void by inviting some of the prominent researchers in Biometrics to contribute chapters describing the fundamentals as well as the latest innovations in their respective areas of expertise.
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paper_title: Live face detection based on the analysis of Fourier spectra

paper_content:

Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.
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paper_title: Face spoofing detection from single images using micro-texture analysis

paper_content:

Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.
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paper_title: Face liveness detection based on texture and frequency analyses

paper_content:

This paper proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Still images taken from live faces and 2-D paper masks were found to bear the differences in terms of shape and detailedness. In order to effectively employ such differences, we exploit frequency and texture information by using power spectrum and Local Binary Pattern (LBP), respectively. In the experiments, three liveness detectors utilizing the power spectrum, LBP, and fusion of the two were trained and tested with two databases which consist of images taken from live and four types of 2-D paper masks. One database was acquired from a web camera while the other was from the camera on the automated teller machine. Experimental results show that the proposed methods can efficiently classify 2-D paper masks and live faces.
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paper_title: Face liveness detection using variable focusing

paper_content:

As Face Recognition(FR) technology becomes more mature and commercially available in the market, many different anti-spoofing techniques have been recently developed to enhance the security, reliability, and effectiveness of FR systems. As a part of anti-spoofing techniques, face liveness detection plays an important role to make FR systems be more secured from various attacks. In this paper, we propose a novel method for face liveness detection by using focus, which is one of camera functions. In order to identify fake faces (e.g. 2D pictures), our approach utilizes the variation of pixel values by focusing between two images sequentially taken in different focuses. The experimental result shows that our focus-based approach is a new method that can significantly increase the level of difficulty of spoof attacks, which is a way to improve the security of FR systems. The performance is evaluated and the proposed method achieves 100% fake detection in a given DoF(Depth of Field).
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paper_title: Evaluating liveness by face images and the structure tensor

paper_content:

A technique evaluating liveness in short face image sequences is presented The intended purpose of the proposed system is to assist in a biometric authentication framework, by adding liveness awareness in a non-intrusive manner. Analyzing the trajectories of single parts of a live face reveal valuable information to discriminate it against a spoofed one. The proposed system uses a lightweight novel optical flow, which is especially applicable in face motion estimation based on the structure tensor and a few frames. It uses a model-based local Gabor decomposition and SVM experts for face part detection. An alternative approach for face pan detection using optical flow pattern matching is introduced as well. Experimental results on the proposed system are presented.
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paper_title: A liveness detection method for face recognition based on optical flow field

paper_content:

It is a common spoof to use a photograph to fool face recognition algorithm. In light of differences in optical flow fields generated by movements of two-dimensional planes and three-dimensional objects, we proposed a new liveness detection method for face recognition. Under the assumption that the test region is a two-dimensional plane, we can obtain a reference field from the actual optical flow field data. Then the degree of differences between the two fields can be used to distinguish between a three-dimensional face and a two-dimensional photograph. Empirical study shows that the proposed approach is both feasible and effective.
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paper_title: Blinking-based live face detection using conditional random fields

paper_content:

This paper presents a blinking-based liveness detection method for human face using Conditional Random Fields (CRFs). Our method only needs a web camera for capturing video clips. Blinking clue is a passive action and does not need the user to to any hint, such as speaking, face moving. We model blinking activity by CRFs, which accommodates long-range contextual dependencies among the observation sequence. The experimental results demonstrate that the proposed method is promising, and outperforms the cascaded Adaboost method and HMM method.
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paper_title: Face liveness detection with component dependent descriptor

paper_content:

Spoofing attacks mainly include printing artifacts, electronic screens and ultra-realistic face masks or models. In this paper, we propose a component-based face coding approach for liveness detection. The proposed method consists of four steps: (1) locating the components of face; (2) coding the low-level features respectively for all the components; (3) deriving the high-level face representation by pooling the codes with weights derived from Fisher criterion; (4) concatenating the histograms from all components into a classifier for identification. The proposed framework makes good use of micro differences between genuine faces and fake faces. Meanwhile, the inherent appearance differences among different components are retained. Extensive experiments on three published standard databases demonstrate that the method can achieve the best liveness detection performance in three databases.
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paper_title: Face liveness detection based on texture and frequency analyses

paper_content:

This paper proposes a single image-based face liveness detection method for discriminating 2-D paper masks from the live faces. Still images taken from live faces and 2-D paper masks were found to bear the differences in terms of shape and detailedness. In order to effectively employ such differences, we exploit frequency and texture information by using power spectrum and Local Binary Pattern (LBP), respectively. In the experiments, three liveness detectors utilizing the power spectrum, LBP, and fusion of the two were trained and tested with two databases which consist of images taken from live and four types of 2-D paper masks. One database was acquired from a web camera while the other was from the camera on the automated teller machine. Experimental results show that the proposed methods can efficiently classify 2-D paper masks and live faces.
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paper_title: Liveness detection based on 3D face shape analysis

paper_content:

In recent years face recognition systems have been applied in various useful applications, such as surveillance, access control, criminal investigations, law enforcement, and others. However face biometric systems can be highly vulnerable to spoofing attacks where an impostor tries to bypass the face recognition system using a photo or video sequence. In this paper a novel liveness detection method, based on the 3D structure of the face, is proposed. Processing the 3D curvature of the acquired data, the proposed approach allows a biometric system to distinguish a real face from a photo, increasing the overall performance of the system and reducing its vulnerability. In order to test the real capability of the methodology a 3D face database has been collected simulating spoofing attacks, therefore using photographs instead of real faces. The experimental results show the effectiveness of the proposed approach.
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paper_title: Face liveness detection under bad illumination conditions

paper_content:

Spoofing face recognition systems with photos or videos of someone else is not difficult. Sometimes, all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system. In order to detect this kind of spoofs, in this paper we present a solution that works either with printed or LCD displayed photographs, even under bad illumination conditions without extra-devices or user involvement. Tests conducted on large databases show good improvements of classification accuracy as well as true positive and false positive rates compared to the state-of-the-art.
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paper_title: Face liveness detection from a single image with sparse low rank bilinear discriminative model

paper_content:

Spoofing with photograph or video is one of the most common manner to circumvent a face recognition system. In this paper, we present a real-time and non-intrusive method to address this based on individual images from a generic webcamera. The task is formulated as a binary classification problem, in which, however, the distribution of positive and negative are largely overlapping in the input space, and a suitable representation space is hence of importance. Using the Lambertian model, we propose two strategies to extract the essential information about different surface properties of a live human face or a photograph, in terms of latent samples. Based on these, we develop two new extensions to the sparse logistic regression model which allow quick and accurate spoof detection. Primary experiments on a large photo imposter database show that the proposed method gives preferable detection performance compared to others.
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paper_title: Face liveness detection by exploring multiple scenic clues

paper_content:

Liveness detection is an indispensable guarantee for reliable face recognition, which has recently received enormous attention. In this paper we propose three scenic clues, which are non-rigid motion, face-background consistency and imaging banding effect, to conduct accurate and efficient face liveness detection. Non-rigid motion clue indicates the facial motions that a genuine face can exhibit such as blinking, and a low rank matrix decomposition based image alignment approach is designed to extract this non-rigid motion. Face-background consistency clue believes that the motion of face and background has high consistency for fake facial photos while low consistency for genuine faces, and this consistency can serve as an efficient liveness clue which is explored by GMM based motion detection method. Image banding effect reflects the imaging quality defects introduced in the fake face reproduction, which can be detected by wavelet decomposition. By fusing these three clues, we thoroughly explore sufficient clues for liveness detection. The proposed face liveness detection method achieves 100% accuracy on Idiap print-attack database and the best performance on self-collected face anti-spoofing database.
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paper_title: Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

paper_content:

A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.
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paper_title: Context based face anti-spoofing

paper_content:

The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks and the number of countermeasures is gradually increasing. Fairly good results have been reported on the publicly available databases but it is reasonable to assume that there exists no superior anti-spoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, we propose to approach the problem of face spoofing as a set of attack-specific subproblems that are solvable with a proper combination of complementary countermeasures. Inspired by how we humans can perform reliable spoofing detection only based on the available scene and context information, this work provides the first investigation in research literature that attempts to detect the presence of spoofing medium in the observed scene. We experiment with two publicly available databases consisting of several fake face attacks of different nature under varying conditions and imaging qualities. The experiments show excellent results beyond the state of the art. More importantly, our cross-database evaluation depicts that the proposed approach has promising generalization capabilities.
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paper_title: Verifying liveness by multiple experts in face biometrics

paper_content:

Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the ldquolivenessrdquo topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of anti-spoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.
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Title: An Overview of Face Liveness Detection

Section 1: INTRODUCTION
Description 1: Write about the importance of face liveness detection, the security challenges associated with face recognition, and the need for distinguishing between live and non-live faces.

Section 2: LITERATURE
Description 2: Present a summary of the various face liveness detection methods.

Section 3: Frequency and Texture based analysis
Description 3: Describe methods that use frequency and texture analyses to differentiate live faces from fake ones.

Section 4: Variable Focusing based analysis
Description 4: Discuss techniques that rely on capturing images at different focus levels to detect liveness based on depth information.

Section 5: Movement of the eyes based analysis
Description 5: Explain approaches that detect liveness through variations and movement in the eye regions.

Section 6: Optical Flow based analysis
Description 6: Cover methods that use the properties of optical flow fields generated from 3D objects and 2D planes to detect liveness.

Section 7: Blinking based analysis
Description 7: Explore techniques that use blinking detection to ascertain face liveness.

Section 8: Component Dependent Descriptor based analysis
Description 8: Detail methods that code different facial components to distinguish live faces from fake ones.

Section 9: 3D Face Shape based analysis
Description 9: Discuss the techniques that use 3D facial structures to detect liveness.

Section 10: Binary Classification based analysis
Description 10: Describe methods that treat face liveness detection as a binary classification problem.

Section 11: Scenic Clues based analysis
Description 11: Explain approaches that detect liveness by analyzing multiple contextual and scenic clues.

Section 12: Lip Movement based analysis
Description 12: Present methods that rely on lip movement classification and lip reading for liveness detection.

Section 13: Context based analysis
Description 13: Discuss the utilization of scene and context information for detecting face spoofing.

Section 14: Combination of Standard Techniques based analysis
Description 14: Provide details on methods that combine various standard techniques for face liveness detection.

Section 15: DISCUSSION
Description 15: Summarize the different approaches, comparing their strengths and limitations, and discuss the overall trends in face liveness detection research.