Improving Face Anti-Spoofing by 3D Virtual Synthesis

Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be re-printed and re-captured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face anti-spoofing using cheap and large-scale synthetic data.Comment: Accepted to ICB 201

An improved parallel thinning algorithm

This paper describes an improved thinning algorithm for binary images. We improve thinning algorithm from the fundamental properties such as connectivity, onepixel width, robust to noise and speed. In addition, in order to overcome information loss, we integrated the contour and skeleton of pattern and proposed the threshold way. Some fundamental requirements of thinning and the shape of pattern are preserved very well. Algorithm is very robust to noise and eliminate some spurious branch. Above all, it can overcome the loss of information in pattern. Experimental results show the performance of the proposed algorithm. 1