Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> A system that combines real-time face tracking as well as the localization of facial landmarks in order to improve the authenticity of fingerprint recognition is introduced. The intended purpose of this application is to assist in securing public areas and individuals, in addition to enforce that the collected sensor data in a multi modal person authentication system originate from present persons, i.e. the system is not under a so called play back attack. Facial features are extracted with the help of Gabor filters and classified by SVM experts. For real-time performance, selected points from a retinotopic grid are used to form regional face models. Additionally only a subset of the Gabor decomposition is used for different face regions. The second modality presented is texture-based fingerprint recognition, exploiting linear symmetry. Experimental results on the proposed system are presented. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection. <s> BIB002 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> 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. <s> BIB003 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> Abstract In recent years, face recognition has often been proposed for personal identification. However, there are many difficulties with face recognition systems. For example, an imposter could login the face recognition system by stealing the facial photograph of a person registered on the facial recognition system. The security of the face recognition system requires a live detection system to prevent system login using photographs of a human face. This paper describes an effective, efficient face live detection method which uses physiological motion detected by estimating the eye blinks from a captured video sequence and an eye contour extraction algorithm. This technique uses the conventional active shape model with a random forest classifier trained to recognize the local appearance around each landmark. This local match provides more robustness for optimizing the fitting procedure. Tests show that this face live detection approach successfully discriminates a live human face from a photograph of the registered person's face to increase the face recognition system reliability. <s> BIB004 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> 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. <s> BIB005 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> A common technique to by-pass 2-D face recognition systems is to use photographs of spoofed identities. Unfortunately, research in counter-measures to this type of attack have not kept-up - even if such threats have been known for nearly a decade, there seems to exist no consensus on best practices, techniques or protocols for developing and testing spoofing-detectors for face recognition. We attribute the reason for this delay, partly, to the unavailability of public databases and protocols to study solutions and compare results. To this purpose we introduce the publicly available PRINT-ATTACK database and exemplify how to use its companion protocol with a motion-based algorithm that detects correlations between the person's head movements and the scene context. The results are to be used as basis for comparison to other counter-measure techniques. The PRINT-ATTACK database contains 200 videos of real-accesses and 200 videos of spoof attempts using printed photographs of 50 different identities. <s> BIB006 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> In this paper, a motion and similarity-based fake detection algorithm is presented for biometric face recognition systems. First, an input video is segmented into foreground and background regions. Second, the similarity is measured between a background region, i.e., a region without a face and upper body, and an original background region recorded at an initializing stage. Third, a background motion index is calculated to indicate the amount of motion in the background region compared with the motion in the foreground region. By combining the result of similarity and the result of the background motion index, a fake video can be detected robustly with a regular USB camera. <s> BIB007 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> This paper presents a liveness detection method based on tracking the gaze of the user of a face recognition system using a single camera. The user is required to follow a visual animation of a moving object on a display screen while his/her gaze is measured. The visual stimulus is designed to direct the gaze of the user to sets of collinear points on the screen. Features based on the measured collinearity of the observed gaze are then used to discriminate between live attempts at responding to this challenge and those conducted by âimpostorsâ holding photographs and attempting to follow the stimulus. An initial set of experiments is reported that indicates the effectiveness of the proposed method in detecting this class of spoofing attacks. <s> BIB008 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> Face recognition provides many advantages compared with other available biometrics, but it is particularly subject to spoofing. The most accurate methods in literature addressing this problem, rely on the estimation of the three-dimensionality of faces, which heavily increase the whole cost of the system. This paper proposes an effective and efficient solution to problem of face spoofing. Starting from a set of automatically located facial points, we exploit geometric invariants for detecting replay attacks. The presented results demonstrate the effectiveness and efficiency of the proposed indices. <s> BIB009 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> 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. <s> BIB010 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> Recent advances on biometrics, information forensics, and security have improved the accuracy of biometric systems, mainly those based on facial information. However, an ever-growing challenge is the vulnerability of such systems to impostor attacks, in which users without access privileges try to authenticate themselves as valid users. In this work, we present a solution to video-based face spoofing to biometric systems. Such type of attack is characterized by presenting a video of a real user to the biometric system. To the best of our knowledge, this is the first attempt of dealing with video-based face spoofing based in the analysis of global information that is invariant to video content. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access. To capture the noise and obtain a compact representation, we use the Fourier spectrum followed by the computation of the visual rhythm and extraction of the gray-level co-occurrence matrices, used as feature descriptors. Results show the effectiveness of the proposed approach to distinguish between valid and fake users for video-based spoofing with near-perfect classification results. <s> BIB011 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> While there is a significant number of works addressing e.g. pose and illumination variation problems in face recognition, the vulnerabilities to spoofing attacks were mostly unexplored until very recently when an increasing attention is started to be paid to this threat. A spoofing attack occurs when a person tries to masquerade as someone else e.g. by wearing a mask to gain illegitimate access and advantages. This work provides the first investigation in research literature on the use of dynamic texture for face spoofing detection. Unlike masks and 3D head models, real faces are indeed non-rigid objects with contractions of facial muscles which result in temporally deformed facial features such as eye lids and lips. Our key idea is to learn the structure and the dynamics of the facial micro-textures that characterise only real faces but not fake ones. Hence, we introduce a novel and appealing approach to face spoofing detection using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern approach. We experiment with two publicly available databases consisting of several fake face attacks of different natures under varying conditions and imaging qualities. The experiments show excellent results beyond the state-of-the-art. <s> BIB012 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> Face recognition, which is security-critical, has been widely deployed in our daily life. However, traditional face recognition technologies in practice can be spoofed easily, for example, by using a simple printed photo. In this paper, we propose a novel face liveness detection approach to counter spoofing attacks by recovering sparse 3D facial structure. Given a face video or several images captured from more than two viewpoints, we detect facial landmarks and select key frames. Then, the sparse 3D facial structure can be recoveredfrom the selected key frames. Finally, an Support Vector Machine (SVM) classifier is trained to distinguish the genuine and fake faces. Compared with the previous works, the proposed method has the following advantages. First, it gives perfect liveness detection results, which meets the security requirement of face biometric systems. Second, it is independent on cameras or systems, which works well on different devices. Experiments with genuine faces versus planar photo faces and warped photo faces demonstrate the superiority of the proposed method over the state-of-the-art liveness detection methods. <s> BIB013 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP−TOP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15.16% to 7.60%. <s> BIB014 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> For a robust face biometric system, a reliable anti-spoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance, especially HOOF descriptor yielding a near perfect half total error rate of 0%and 1.25% respectively. <s> BIB015 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) FACE ANTI-SPOOFING: FEATURE LEVEL DYNAMIC APPROACHES <s> Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our face images and videos are wide-spread on the internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy – we lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we propose a new technique of counter-measure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this article. <s> BIB016
One of the pioneer feature-level protection approaches in 2D face recognition appeared as a countermeasure to the first attacks studied which made use of static face printouts (i.e., print attacks). Such anti-spoofing techniques, which still remain quite popular against print attacks, rely on the detection of motion over a face video sequence. In particular, they are based on the trajectory analysis of specific face segments. Such dynamic features reveal valuable information to discriminate between real faces and spoofed static copies. Typical cues used in this type of anti-spoofing methods, some of them involving challenge-response strategies, are: eye blinking BIB003 , BIB002 ,  - BIB004 ; face and head gestures (e.g., nodding, smiling, looking in different directions) detected trough face and gaze tracking BIB001 , BIB008 or through optical flow estimation  ,  , BIB005 , BIB016 . These techniques are usually highly effective to detect photo-attacks but lose accuracy against illegal access attempts carried out with replayed videos where not only the face appearance but also its movements are forged. In order to overcome this shortcoming, some dynamic liveness detection techniques have been specifically proposed to detect video-based attacks: exploiting the 3D structure of the face through the analysis of several 2D images with different head poses BIB009 , BIB013 ; using context-based analysis in order to take advantage of non-facial information available from acquired samples such as motion features from the scene (e.g., background vs foreground motion) BIB006 , BIB007 - BIB010 ; estimating the noise produced during the recapturing process (i.e., fixed pattern noise and the noise resulting from the photo-responsiveness of nonuniform light-sensitive cells) BIB011 ; using modified versions of the popular Local Binary Patterns (LBP), in order to take into account temporal information present in video sequences BIB014 or to analyse the dynamics of facial texture in comparison to rigid objects such as photos or masks BIB012 ; applying the recently proposed Eulerian video magnification algorithm to enhance motion in video as a previous step to anti-spoofing feature extraction BIB015 . Given that they are designed to exploit both spatial and temporal information of face videos, dynamic-based antispoofing schemes usually achieve very competitive performance. However, as a limitation, they cannot be used in systems where only a single face image of the user is available (e.g., passport related applications). Moreover, even in scenarios where video data has been recorded (e.g., surveillance applications), it is not rare to find that only a very few non-consecutive frames are suitable for facial analysis, which also limits their final use and accuracy.