Camera Invariant Feature Learning for Generalized Face Anti-Spoofing

Anti-spoofing, Face

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...

Illumination Invariant Feature Selection for Face Recognition

We propose a novel hybrid illumination invariant feature selection scheme for face recognition, which is a combination of geometrical feature extraction and linear subspace projection. By local geometry feature enhancement technique, neighborhood histogram equalization (NHE) in our experiment, some illegible edges due to week illumination will be enhanced effectively. Then we applied classic li...

Feature Selection for Pose Invariant Face Recognition

One of the major difficulties in face recognition systems is the in-depth pose variation problem. Most face recognition approaches assume that the pose of the face is known. In this work, we have designed a feature based pose estimation and face recognition system using 2D Gabor wavelets as local feature information. The difference of our system from the existing ones lies in its simplicity and...

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....