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Tiny-FASNet: A Tiny Face Anti-spoofing Method Based on Tiny Module

  • Ce Li*
  • , Enbing Chang
  • , Fenghua Liu
  • , Shuxing Xuan
  • , Jie Zhang
  • , Tian Wang
  • *Corresponding author for this work
  • Lanzhou University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Face Anti-spoofing (FAS) has arisen as one of the essential issues in face recognition systems. The existing deep learning FAS methods have achieved outstanding performance, but most of them are too complex to be deployed in embedded devices. Therefore, a tiny single modality FAS method (Tiny-FASNet) is proposed. First, to reduce the complexity, the tiny module is presented to simulate fully convolution operations. Specifically, some intrinsic features extracted by convolution are used to generate more features through cheap linear transformations. Besides, a simplified streaming module is proposed to keep more spatial structure information for FAS task. All models are trained and tested on depth images. The proposed model achieves 0.0034(ACER), 0.9990(TPR@FPR = 10E–2), and 0.9860(TPR@FPR = 10E–3) on CASIA-SURF dataset only with 0.018M parameters and 12.25M FLOPS. Extensive evaluations in two publicly available datasets (CASIA-SURF and CASIA-SURF CeFA) demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 4th Chinese Conference, PRCV 2021, Proceedings
EditorsHuimin Ma, Liang Wang, Changshui Zhang, Fei Wu, Tieniu Tan, Yaonan Wang, Jianhuang Lai, Yao Zhao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages362-373
Number of pages12
ISBN (Print)9783030880095
DOIs
StatePublished - 2021
Event4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021 - Beijing, China
Duration: 29 Oct 20211 Nov 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13021 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021
Country/TerritoryChina
CityBeijing
Period29/10/211/11/21

Keywords

  • Depth image
  • Face Anti-spoofing
  • Tiny models

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