1. Scene-Independent Feature Representation for Face Anti-Spoofing
- Author
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Hui-Ming Ding, You-Bao Wang, Wen-Shuai Qi, and Xie Zhifeng
- Subjects
021110 strategic, defence & security studies ,Spoofing attack ,Generalization ,business.industry ,Computer science ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Anti spoofing ,Face (geometry) ,Benchmark (computing) ,Feature (machine learning) ,Artificial intelligence ,business ,Adaptation (computer science) ,Representation (mathematics) ,0105 earth and related environmental sciences - Abstract
Face anti-spoofing, as a security measure for face verification and recognition system, could distinguish between genuine and fake faces. Although some impressive results have been achieved by CNN when evaluated on intra-tests (i.e. the model is trained and tested on the same dataset). Unfortunately, most of these models fail to generalize well to unseen attacks (e.g. when the model is trained on one dataset and then evaluated on another dataset). This is a major concern in face anti-spoofing research which is mostly overlooked. Our experiments on two challenging benchmark face spoofing datasets, CASIA and Replay-Attack, demonstrate the disappointed adaptation ability for CNN from one dataset to another. Via visualizing the implicit attention of CNN, we find that scene-dependent features extracted by CNN affect models generalization capabilities. To address this problem, we propose a novel solution based on applying scene-independent features representation. more...
- Published
- 2018
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