Auto-Fas : searching lightweight networks for face anti-spoofing |
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Author: | Yu, Zitong1; Qin, Yunxiao2; Xu, Xiaqing3; |
Organizations: |
1CMVS, University of Oulu 2Northwestern Polytechnical University 3Aibee
4NLPR, Institute of Automation, Chinese Academy of Sciences
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Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 2 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe2020112092121 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2020
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Publish Date: | 2020-11-20 |
Description: |
AbstractWith 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. see all
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Series: |
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing |
ISSN: | 1520-6149 |
ISSN-E: | 2379-190X |
ISSN-L: | 1520-6149 |
ISBN: | 978-1-5090-6631-5 |
ISBN Print: | 978-1-5090-6632-2 |
Pages: | 996 - 1000 |
DOI: | 10.1109/ICASSP40776.2020.9053587 |
OADOI: | https://oadoi.org/10.1109/ICASSP40776.2020.9053587 |
Host publication: |
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Conference: |
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 |
Subjects: | |
Funding: |
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 |
Detailed Information: |
316765 (Academy of Finland Funding decision) |
Copyright information: |
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