Title:	Anomaly Detection with Transformers in Face Anti-spoofing
Authors:	Abduh, Latifah Luma, Omar Ivrissimtzis, Ioannis
Citation:	Journal of WSCG. 2023, vol. 31, no. 1-2, p. 91-98.
Issue Date:	2023
Publisher:	Václav Skala - UNION Agency
Document type:	article článek
URI:	http://hdl.handle.net/11025/54288
ISSN:	1213 – 6972 (hard copy) 1213 – 6980 (CD-ROM) 1213 – 6964 (on-line)
Keywords:	útok na prezentaci obličeje;transformátor vidění;ResNet;detekce anomálií;jednotřídní klasifikace
Keywords in different language:	face presentation attack;vision transformer;ResNet;anomaly detection;one-class classification
Abstract in different language:	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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	Volume 31, Number 1-2 (2023)

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.