Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC 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> BIB002 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB003 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> Face recognition is an increasingly popular method for user authentication. However, face recognition is susceptible to playback attacks. Therefore, a reliable way to detect malicious attacks is crucial to the robustness of the system. We propose and validate a novel physics-based method to detect images recaptured from printed material using only a single image. Micro-textures present in printed paper manifest themselves in the specular component of the image. Features extracted from this component allows a linear SVM classifier to achieve 2.2% False Acceptance Rate and 13% False Rejection Rate (6.7% Equal Error Rate). We also show that the classifier can be generalizable to contrast enhanced recaptured images and LCD screen recaptured images without re-training, demonstrating the robustness of our approach.1 <s> BIB004 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB005 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> Personal identity verification based on biometrics has received increasing attention since it allows reliable authentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving, law enforcement, among others. To strengthen the results of verification, biometric systems must be robust against spoofing attempts with photographs or videos, which are two common ways of bypassing a face recognition system. In this paper, we describe an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos. The proposed method explores both spatial and temporal information to learn distinctive characteristics between the two classes. Experiments conducted to validate our solution with datasets containing images and videos show results comparable to state-of-the-art approaches. <s> BIB006 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB007 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks. <s> BIB008 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature. <s> BIB009 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types. <s> BIB010 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> In this paper, a new face anti-spoofing approach, which is based on analysis of contrast and texture characteristics of captured and recaptured images, is proposed to detect photograph spoofing. Since photo image is a recaptured image, it may show quite different contrast and texture characteristics when compared to a real face image. In a spoofing attempt, image rotation is quite possible. Therefore, in this paper, a rotation invariant local binary pattern variance (LBPV) based method is selected to be used. The approach is tested on the publicly available NUAA photo-impostor database, which is constructed under illumination and place change. The results show that the approach is competitive with other existing methods tested on the same database. It is especially useful for conditions when photos are held by hand to spoof the system. Since an LBPV based method is used, it is robust to illumination changes. It is non-intrusive and simple. <s> BIB011 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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, characterisation of printing artefacts and differences in light reflection, the authors 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 and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust 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 three publicly available databases showed excellent results compared to existing works. <s> BIB012 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB013 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB014 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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. <s> BIB015 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> 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). <s> BIB016 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) FACE ANTI-SPOOFING: FEATURE LEVEL STATIC APPROACHES <s> As a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive inform of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper. <s> BIB017
As mentioned on the previous section, dynamic anti-spoofing schemes need a temporal face sequence of sufficient duration to achieve high accuracy. This restriction has motivated the appearance of a second group of approaches for the detection of spoofing access attempts to 2D face recognition systems, focused on the analysis of a single static image and not of video data. These techniques are in general faster than their dynamic counterparts and, therefore, more convenient for the user, at the cost, in some cases, of a certain performance loss. The vast majority of feature-level static methods are based on the analysis of the face texture using different image processing tools such as: the Fourier Spectrum BIB001 ; multiple Difference of Gaussian (DoG) filters to extract specific frequency information BIB009 which has also been combined with features obtained from the Lambertian model BIB003 proving remarkable performance even under bad illumination conditions BIB005 ; partial least squares to analyse specific information from low-level descriptors BIB006 ; VOLUME 2, 2014 TABLE 2. Summary of the most relevant face anti-spoofing techniques presented in Sect. III. Column ''subtype'' shows the algorithm subtype within each of the three main categories considered in the work (sensor-level, feature-level and score-level) as shown in the taxonomy in Fig. 3 . Column ''attack'' refers to the type of face spoofing attacks considered in the work as defined in Sect. III-A: photo, video or mask attacks. for clarity, public databases are just referenced the first time they appear in the table, in successive entries only their name is given (for further information on these public databases please see Sect. IV-C). Database sizes that appear in column ''database'' are approximate and are given just as an indication of their order of magnitude (for the exact structure and size the reader should consult the corresponding reference). The same applies for results, as the ones shown in the table are an approximation of the different scenarios considered in each work. Works are ordered chronologically within the same subtype. combination of low-level and high-level face component descriptors BIB014 ; a recent trend based on the use of Local Binary Patterns (LBP) to detect photo-attacks BIB010 , BIB011 , which has been successfully combined with other texture descriptors such as Gabor Wavelets and with shape related information extracted using Histogram Oriented Gradients (HOG) in BIB012 ; detection of paper microtextures present in print-attacks either by analysing the specular components of the facial image BIB004 or by using LBPs as features BIB010 , BIB007 ; as in the case of video sequences, context-based approaches have also been proposed for the static scenario, focusing in this case on the detection of the user's upper body and the attack support (e.g., paper or tablet) BIB015 ; or pixel difference evaluation between two consecutive pictures taken with different focus values BIB016 (although this last method requires two different face images, we include it in the static category as it does not use any temporal information). These static-based anti-spoofing approaches may as well be applied to the case in which a video sequence is available. In this scenario, the analysis is performed on a frame-byframe basis, using fusion techniques (e.g., majority voting) in a later stage to combine the individual scores obtained from each frame in order to generate a unique final decision. Although such a strategy is feasible, in general, it is less efficient than the schemes specifically designed to work with videos, as no temporal information is exploited (e.g., trajectories of facial features). Some of the previous techniques have been successfully fused at feature level showing improved accuracy compared to individual parameters BIB002 , BIB013 . A comparative study of several of these dynamic and static approaches may be found in the 2011 and 2013 Competitions on Countermeasures to 2D facial spoofing attacks BIB017 , BIB008 , where it was shown that, wherever possible (i.e., scenarios with availability of facial video data), the feature-level fusion of both types of techniques (static and dynamic) draws the best performance. Further reading on the results and databases used in these competitions may be found in Sect. IV-C.