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论文范文
1. Introduction Inthe last years, face recognitionsystems have gained interestdue to face’s rich features that offer a strong biometric cue to recognize individuals for a wide variety of applications in both law and nonlaw enforcements [1]. In fact, facial recognition systems are already in operation worldwide, including USVISIT, which is a US Customs and Border Protection (CBP) management system, UIDAI that provides identity to all persons resident in India, and Microsoft Kinectwhich uses face recognition to access dashboard and automatic sign-in to Xbox Live profile. Similarly, face biometrics is also nowadays being used ubiquitously as an alternative to passwords on mobile devices such as Android KitKat mobile OS, Lenovo VeriFace, Asus SmartLogon, and Toshiba SmartFace. Despite the great deal of progress in facial recognition systems, vulnerabilities to face spoof attacks are mainly overlooked [2]. Facial spoof attack is a process in which a fraudulent user can subvert or attack a face recognition system by masquerading as registered user and thereby gaining illegitimate access and advantages [1, 3–5]. Face spoofing attack is a major issue for companies selling face biometricbased identity management solutions [6]. For instance, at New York City, nonwhite robbers disguised themselves as white cops, using life-like latex masks, and were caught robbing a cash-checking store in 2014 (see Figure 1, also for other recent face spoof attacks). Recent study reported in [1] suggests that the successrate of face spoof attacks could be up to 70%, even when a state-of-the-art Commercial Off-The-Shelf (COTS) face recognition system is used. Therefore, we could infer that even COTS face recognition systems are mainly not devised to effectively distinguish spoof faces from genuine live faces.As a matter of fact, this vulnerability of face spoofing to face recognition systems is now enlisted in the National Vulnerability Database of the National Institute of Standards and Technology (NIST) in the US. Typical countermeasure to face spoof attacks is liveness detection method, which aims at disambiguating human live face samples from spoof artifacts [2,7].There existseveral face antispoofing or liveness detection techniques [7-15].However, face spoofing attacks remain a problem due to difficulties in finding discriminative and computationally inexpensive features and techniques for spoof recognition. Moreover, publishedmethods are limited in their scope since they mainly use whole face image or complete video for liveness detection. Nevertheless, often certain face image regions (video frames) are redundant or correspond to the clutter in the image (video), leading thus generally to lowperformances. It is thus essential to develop robust, efficient, and compact face antispoofing (or liveness detection) methods, which are capable of generalizing well to discriminative,class-specific information and imaging conditions. To this aim, in this paper, we propose a simple and effective solution based on discriminative image patches. In particular, we propose seven novel fully automated algorithms to highlight regions of interest in face images. We define these regions (or image patches) to be discriminative (i.e., specific to a particular class: live or spoof), consistent (i.e., reliably appearing in different face images or video frames), salient (i.e., conspicuous regions), and repetitive (i.e., frequently appearing in the image set of specific class). The basic notion is “interesting patches are those that are specific to a face image (or video frame) and should contain features that give assistance to discriminate a given live face image from spoofed one.” Based on this definition, two of the seven proposed image patch selectionmethods (i.e.,MAXDIST and DEND-CLUSTER) do not employ any training or prior learning. However, the remaining techniques use simple clustering (i.e., CP and CS), image intensity (i.e., IPI), image quality (i.e., IQA), or diversity filter (i.e.,DF) to obtain discriminative patches. For final classification, we exploited four wellknown classifiers, namely, support vector machine (SVM), Naive-Bayes, Quadratic Discriminant Analysis (QDA), and Ensemble, using voting based scheme. Experimental analysis on two publicly available databases (Idiap Replay-Attack and CASIA-FASD) shows good results compared to existing works. 
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