One-Class Learning Method Based on Live Correlation Loss for Face Anti-Spoofing

Anti-spoofing, Face

A Novel Face Spoofing Detection Method Based on Gaze Estimation

Since gaze is a kind of behavioral biometrics which is difficult to be detected by the surveillance due to the ambiguity of visual attention process, it can be used as a clue for anti-spoofing. This work provides the first investigation in research literature on the use of gaze estimation for face spoofing detection. Firstly, a gaze estimation model mapping the gaze feature to gaze position is ...

Learn Convolutional Neural Network for Face Anti-Spoofing

Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some...

Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision

Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues....

A Class-Incremental Learning Method Based on One Class Support Vector Machine

A method based on one class support vector machine (OCSVM) is proposed for class incremental learning. Several OCSVM models divide the input space into several parts. Then, the 1VS1 classifiers are constructed for the confuse part by using the support vectors. During the class incremental learning process, the OCSVM of the new class is trained at first. Then the support vectors of the old class...