Anomaly Detection with Transformers in Face Anti-spoofing

Abduh, Latifah; Omar, Luma; Ivrissimtzis, Ioannis

Abstract

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNet as the backbone for anomaly detection in face anti-spoofing, and compare the performance of various one-class classifiers at the end of the pipeline, such as one-class SVM, Isolation Forest, and decoders. Test results on the RA and SiW databases show the proposed approach to be competitive as an anomaly detection method for face anti-spoofing.

Citation

Abduh, L., Omar, L., & Ivrissimtzis, I. (2023, May). Anomaly Detection with Transformers in Face Anti-spoofing. Presented at WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023, Plzen, Czech Republic

• Presentation Conference Type: Conference Paper (published)
• Conference Name: WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023
• Start Date: May 15, 2023
• End Date: May 19, 2023
• Acceptance Date: Apr 21, 2023
• Publication Date: 2023
• Deposit Date: Jun 15, 2023
• Publicly Available Date: Jul 27, 2023
• Publisher: WSCG
• Volume: 30
• Series ISSN: 1213-6972
• DOI: https://doi.org/10.24132/JWSCG.2023.10
• Public URL: https://durham-repository.worktribe.com/output/1133859
• Publisher URL: http://wscg.zcu.cz/DL/wscg_DL.htm