Visible to the public Learning by Environment Clusters for Face Presentation Attack Detection

TitleLearning by Environment Clusters for Face Presentation Attack Detection
Publication TypeConference Paper
Year of Publication2021
AuthorsMatsunami, Tomoaki, Uchida, Hidetsugu, Abe, Narishige, Yamada, Shigefumi
Conference Name2021 International Conference of the Biometrics Special Interest Group (BIOSIG)
Keywordsattack vectors, Face anti-spoofing, face image clustering, face recognition, Focusing, Human Behavior, Instruments, lighting, presentation attack detection, pubcrawl, Resiliency, Scalability, Support vector machines, Three-dimensional displays, Training data
AbstractFace recognition has been used widely for personal authentication. However, there is a problem that it is vulnerable to a presentation attack in which a counterfeit such as a photo is presented to a camera to impersonate another person. Although various presentation attack detection methods have been proposed, these methods have not been able to sufficiently cope with the diversity of the heterogeneous environments including presentation attack instruments (PAIs) and lighting conditions. In this paper, we propose Learning by Environment Clusters (LEC) which divides training data into some clusters of similar photographic environments and trains bona-fide and attack classification models for each cluster. Experimental results using Replay-Attack, OULU-NPU, and CelebA-Spoof show the EER of the conventional method which trains one classification model from all data was 20.0%, but LEC can achieve 13.8% EER when using binarized statistical image features (BSIFs) and support vector machine used as the classification method.
DOI10.1109/BIOSIG52210.2021.9548300
Citation Keymatsunami_learning_2021