With the development of mobile devices, it is hopeful and pressing to deploy face recognition and face anti-spoofing (FAS) model on cell phone or portable devices. Most of existing face anti-spoofing methods focus on building computational costly detector for better spoofing face detection performance. However, these detectors are unfriendly to be deployed on the mobile device for real-time FAS applications. In this paper, we propose a neural architecture search (NAS) based method called Auto-FAS, intending to discover well-suitable lightweight networks for mobile-level face anti-spoofing. In Auto-FAS, a special search space is designed to restrict the model’s size, and pixel-wise binary supervision is used to improve the model’s performance. We demonstrate both the effectiveness and efficiency of the proposed approach on three public benchmark datasets, which shows the potential real-time FAS application for mobile devices.
Yu Zitong, Qin Yunxiao, Xu Xiaqing, Zhao Chenxu, Wang Zezheng, Lei Zhen, Zhao Guoying
A4 Article in conference proceedings
Place of publication:
ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Z. Yu et al., “Auto-Fas: Searching Lightweight Networks for Face Anti-Spoofing,” ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 996-1000, doi: 10.1109/ICASSP40776.2020.9053587
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