Face anti-spoofing based on multi-model features

Abstract

As face anti-spoofing is becoming a more and more popular technology, it is important to protect face recognition systems from the attack. There are many available face anti-spoofing benchmark datasets used for face anti-spoofing this recently. In this thesis, I firstly introduce some basic knowledge and developing history of face recognition technology. At the moment, face recognition technology is wildly used in various fields, such as railway security system, education and smart city construction. However, face recognition systems currently are easy to be attacked by various of methods, including photo attack, video clips and two dimension or three-dimension mask, causing the recognition result more unreliable. In order to solve this problem, face anti-spoofing becomes a hot topic. In this thesis, we describe various methods of anti-spoofing methods, including Color Texture, Patch and Depth-Based CNNs, DMD + LBP, Pulse + texture, Deep Pulse and Depth, Micro-texture + SSD and De-Spoofing. After that, we analyze and compare the numbers and characteristics of different face anti-spoofing data sets. We do improvement to the multi-modal fusion method to better combine these four chosen features: RGB, Depth, IR and HSV. They are modaldependent features and are re-weighted in order to choose informative channel features and at the same time suppress the useless ones. Finally, we conduct experiments on CASIA-SURF which is current the most complete multi-modal dataset in the world. The results show that the TPR of squeeze and excitation fusion method are 7.6%, 48.2% and 39.0%, which is better than halfway fusion method with the FPR=0.01, 0.001 and 0.0001, respectively.