Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) YALE-RECAPTURED DB <s> We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render (or synthesize) images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose, the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone. Test results show that the method performs almost without error, except on the most extreme lighting directions. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 2) YALE-RECAPTURED DB <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> BIB002
The YALE-RECAPTURED database BIB002 is publicly available under request from the University of Campinas.  The dataset consists of 640 static images of real access attempts and 1,920 attack samples, acquired from 10 different users. The genuine subset is taken from the previously existing Yale Face DB-B BIB001 , where each user was acquired under 64 different illumination conditions. Attack attempts were carried out displaying real images on three different LCD monitors: i) LG Flatron L196WTQ TABLE 5. Comparative summary of the most relevant features corresponding to the six face spoofing databases described in Sect. IV-C. # indicates number, Samp stands for samples, LQ for 2D low quality, SQ for 2D standard quality, HQ for 2D high quality, Ph for photo, Vd for video, Mk for mask, Hh for handheld, Fx for fixed, Cont for controlled and Adv for adverse. Wide 19'', ii) a CTL 171Lx 1700 TFT, and iii) a DELL Inspiron 1545 notebook. Then, the images were recaptured with two different cameras, a Kodak C813 with a resolution of 8.2 megapixels and a Samsung Omnia i900 of 5 megapixels. After the recapture process, samples were cropped and normalized to grey-scale images of size 64 × 64. The multiple illumination conditions make this database a quite challenging one. However, since the genuine and fake subsets were acquired in different settings and with different devices, results obtained on it may be biased due to these contextual differences (see Sect. IV-B for further reading on anti-spoofing databases acquisition).