Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) NUAA PI DB <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> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 1) NUAA PI DB <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> BIB002
The NUAA Photograph Imposter DB BIB001 is publicly available from the Pattern Recognition and Neural Computing Group of the Nanjing University of Aeronautics an Astronautics (ParNeC-NUAA).  This database contains still images of both real-access and spoofing attack attempts of 15 subjects. Samples are extracted  http://parnec.nuaa.edu.cn/xtan/data/NUAAImposterDB.html HTER stands for half total error rate. for a more detailed description of the database used in the evaluation please see Sect. IV-C.5. The information displayed in the table has been directly extracted from BIB002 . from videos captured with a cheap generic webcam (the model is not specified) with a resolution of 640 × 480 pixels. The database was acquired under uncontrolled illumination conditions in three different sessions separated two weeks from each other. The amount of data among sessions is unbalanced as not all the subjects participated in the three acquisition campaigns. During real access attempts, subjects were asked to stay as still as possible with no evident face movements such as eye blinking, in order to simulate the characteristics of typical print attacks. All attacks were carried out with printed copies of high definition face close-ups of the users, captured with a standard Canon camera (model is not specified). Three different hard-copies of each photograph were generated to attack the system: (i) professional developing on photographic paper of size 6.8cm × 10.2cm; (ii) professional developing on photographic paper of size 8.9cm × 12.7cm; (iii) home generated copy using a standard color ink HP printer and A4 70gr paper. During the attacks the printed images were moved in different forms, trying to imitate the typical behaviour of real access attempts: back and forth, vertically and horizontally, slightly warped around in the vertical and horizontal axis. The database is divided into a train and a test set: the train set comprises images from the two first acquisition sessions, while the test set only contains samples of the last one, therefore there is no overlap between them in terms of samples. However, some users do appear in both sets.