Anomaly Metrics on Class Variations For Face Anti-Spoofing

被引：0
|
作者：
Liu, Weihua [1 ]
Gong, Bing [1 ]
Che, Kai [2 ]
Ma, Jieming [3 ]
Pan, Yushan [3 ]
机构：
[1] School of Information Engineering, Xi’an Eurasia University, Xi’an,710000, China
[2] Research Center, Aviation Industry Corporation of China, Xi’an,710078, China
[3] School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou,215123, China
来源：
Computer Journal | 1600年 / 67卷 / 09期
关键词：
In face anti-spoofing tasks; distinguishing between live and spoof faces across different data domains presents challenges due to inter-class similarities; intra-class variations and unknown spoof patterns. This hampers generalization in real-world applications. To address this; we propose a novel convolutional neural network framework that utilizes spatial-frequency cues for 2D and 3D attacks. Furthermore; we introduce compact anomaly metrics and design three anomaly metrics-based supervisions from the perspective of Reed-Xiaoli anomaly detection; aiming to tackle the challenge posed by unknown attacks. Thanks to our proposed spatial frequency factorization network and its frequency-related supervisions; the spoofing cues are significantly enhanced; resulting in remarkable improvements in our experimental results. These outcomes demonstrate that our proposed framework achieves state-of-the-art performance on both monocular and multi-spectral benchmark datasets. © The British Computer Society 2024. All rights reserved;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Journal article (JA)
引用
收藏
页码：2725 / 2738
相关论文
共 50 条
  • [41] Robust face anti-spoofing with depth information
    Wang, Yan
    Nian, Fudong
    Li, Teng
    Meng, Zhijun
    Wang, Kongqiao
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2017, 49 : 332 - 337
  • [42] Diffusing the Liveness Cues for Face Anti-spoofing
    Li, Sheng
    Zhu, Xun
    Feng, Guorui
    Zhang, Xinpeng
    Qian, Zhenxing
    PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA, MM 2021, 2021, : 1636 - 1644
  • [43] Face De-spoofing: Anti-spoofing via Noise Modeling
    Jourabloo, Amin
    Liu, Yaojie
    Liu, Xiaoming
    COMPUTER VISION - ECCV 2018, PT XIII, 2018, 11217 : 297 - 315
  • [44] A 3D Mask Face Anti-spoofing Database with Real World Variations
    Liu, Siqi
    Yang, Baoyao
    Yuen, Pong C.
    Zhao, Guoying
    PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), 2016, : 1551 - 1557
  • [45] Spoofing Attacks and Anti-Spoofing Methods for Face Authentication Over Smartphones
    Zheng, Zheng
    Wang, Qian
    Wang, Cong
    IEEE COMMUNICATIONS MAGAZINE, 2023, 61 (12) : 213 - 219
  • [46] Fingerprints: Spoofing and anti-spoofing
    Xiao, QH
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2005, 2 : 652 - 657
  • [47] One-Class Face Anti-spoofing Based on Attention Auto-encoder
    Huang, Xiaobin
    Xia, Jingtian
    Shen, Linlin
    BIOMETRIC RECOGNITION (CCBR 2021), 2021, 12878 : 365 - 373
  • [48] Two-Stage Face Detection and Anti-spoofing
    Nurnoby, M. Faisal
    El-Alfy, El-Sayed M.
    ADVANCES IN VISUAL COMPUTING, ISVC 2023, PT I, 2023, 14361 : 445 - 455
  • [49] Face Anti-Spoofing With Deep Neural Network Distillation
    Li, Haoliang
    Wang, Shiqi
    He, Peisong
    Rocha, Anderson
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2020, 14 (05) : 933 - 946
  • [50] Towards Unsupervised Domain Generalization for Face Anti-Spoofing
    Liu, Yuchen
    Chen, Yabo
    Gou, Mengran
    Huang, Chun-Ting
    Wang, Yaoming
    Dai, Wenrui
    Xiong, Hongkai
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2023, : 20597 - 20607
12345