Liveness Detection for Face Recognition in Biometrics: A Review
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paper_title: An Overview of Face Liveness Detection

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Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
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paper_title: Fake Face Detection Based on Skin Elasticity

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Biometric system provides a way of automatic verification or identification a person. But nowadays due to lack of secrecy, there is lot of security threat due to spoofing. Spoofing with photograph or video is one of the most common manners to attack a face recognition system. Liveness detection is a technique that can be used for validating whether the data originate is from a valid user or not. Liveness detection can be hardware based or software based or a combination of both. In this paper, we present a non intrusive and real time method to address this problem, based on skin elasticity of human face. In this technique user is asked to do some movement like chewing and forehead movement simultaneously, so that a full movement to face skin can be given and then sequence of face images is captured with a gap of few milliseconds. Then by applying correlation coefficient between images and then discriminate analysis using some method, face skin is discriminate from the other materials like gelatin, rubber, cadaver, clay etc. In comparison to other face liveness detection, this method will be much user friendly. On the other hand, one of the images captured for liveness detection can be used for face recognition.
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paper_title: Multi-Level Liveness Verification for Face-Voice Biometric Authentication

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In this paper we present the details of the multilevel liveness verification (MLLV) framework proposed for realizing a secure face-voice biometric authentication system that can thwart different types of audio and video replay attacks. The proposed MLLV framework based on novel feature extraction and multimodal fusion approaches, uncovers the static and dynamic relationship between voice and face information from speaking faces, and allows multiple levels of security. Experiments with three different speaking corpora VidTIMIT, UCBN and AVOZES shows a significant improvement in system performance in terms of DET curves and equal error rates (EER) for different types of replay and synthesis attacks.
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paper_title: Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

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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: Robust Audio Visual Biometric Person Authentication with Liveness Verification

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In this paper we propose liveness verification for enhancing the robustness of audio-visual biometric person authentication systems. Liveness verification ensures that biometric cues are acquired from a live person who is actually present at the time of capture for authenticating the identity. The proposed liveness checking technique based on cross-modal association models involves hybrid fusion of acoustic and visual speech correlation features, which measure the degree of synchrony between the lips and the voice extracted from speaking face video sequences. Performance evaluation in terms of DET (Detector Error Tradeoff) curves and EERs (Equal Error Rates) on publicly available audiovisual speech databases show a significant improvement in robustness of system against different types of simulated replay attacks.
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paper_title: Blinking-based live face detection using conditional random fields

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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: Liveness detection based on 3D face shape analysis

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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 spoofing detection from single images using micro-texture analysis

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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

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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: A liveness detection method for face recognition based on optical flow field

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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: Live face detection based on the analysis of Fourier spectra

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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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Title: Liveness Detection for Face Recognition in Biometrics: A Review

Section 1: INTRODUCTION
Description 1: This section introduces the importance of biometrics in security systems, highlights face recognition technology, and explains the challenges such as identity theft and spoofing that face recognition systems encounter. It also outlines the significance of liveness detection and provides an overview of the paper's structure.

Section 2: ARCHITECTURE OF FACE LIVENESS DETECTION SYSTEM
Description 2: This section details the liveness detection process and its integration into biometric systems. It describes the methods (using hardware, software, or both) and the intrinsic properties used for liveness detection. The basic components of a face liveness detection system are also elaborated.

Section 3: RELATED WORK
Description 3: This section reviews various approaches to face liveness detection, categorizing them into intrusive and non-intrusive methods. It presents different techniques and their effectiveness against spoofing attacks along with their limitations.

Section 4: DISCUSSION
Description 4: This section discusses the findings from the reviewed literature, comparing the benefits and drawbacks of intrusive and non-intrusive liveness detection techniques. It provides a comparison of liveness indicators such as motion, texture, and life signs.

Section 5: CONCLUSION
Description 5: This section concludes the paper by summarizing the importance of non-intrusive approaches for liveness detection without using extra hardware. It emphasizes the need for future research to improve the robustness of liveness detection techniques against spoofing attempts.