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
Auto-Fas : searching lightweight networks for face anti-spoofing
|Author:||Yu, Zitong1; Qin, Yunxiao2; Xu, Xiaqing3;|
1CMVS, University of Oulu
2Northwestern Polytechnical University
4NLPR, Institute of Automation, Chinese Academy of Sciences
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020112092121
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-11-20
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.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
|Pages:||996 - 1000|
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE International Conference on Acoustics, Speech and Signal Processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the
Academy of Finland for project MiGA (Grant 316765), ICT 2023 project (Grant 328115), Infotech Oulu and the Chinese National Natural Science Foundation Projects (Grant No. 61876178). As well, the authors acknowledge CSC–IT Center for Science, Finland, for computational resources.
|Academy of Finland Grant Number:||
316765 (Academy of Finland Funding decision)
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.