关键词: 人脸识别, 活体检测, 神经网络, 数据偏置, 泛化性

Abstract: As an uncontrolled, userfriendly biometric verification technology, face recognition is widely used in industries such as public security, finance and personal mobile devices. Face recognition is a typical representative of success computer vision applications for real world problem. Thanks to massive datasets, increasing hardware computing power, and rapid development of deep neural networks technology, deep learning based face verification has made great progress in recognition performance, and has achieved much better performance than the traditional methods. However, the face recognition technology with high recognition accuracy has obvious security holes under malicious forged identity attacks. Techniques such as face security and face antispoofing are attracting more and more attention from both academic and industry. We will first introduce some of the most recent mainstream recognition and antispoofing techniques, and then introduce several challenges in deep learning based face antispoofing, such as insufficient supervision, training data bias, lack of generalization across cameras and lack of representative datasets.

Key words: face recognition, face anti-spoofing, neural network, data bias, generalization