Face anti-spoofing (FAS) plays an important role in protecting face recognition systems from face representation attacks. Many recent studies in FAS have approached this problem with domain generalization technique. Domain generalization aims to increase generalization performance to better detect various types of attacks and unseen attacks. However, previous studies in this area have defined each domain simply as an anti-spoofing datasets and focused on developing learning techniques. In this paper, we proposed a method that enables network to judge its domain by itself with the clustered convolutional feature statistics from intermediate layers of the network, without labeling domains as datasets. We obtained pseudo-domain labels by not only using the network extracting features, but also using depth estimators, which were previously used only as an auxiliary task in FAS. In our experiments, we trained with three datasets and evaluated the performance with the remaining one dataset to demonstrate the effectiveness of the proposed method by conducting a total of four sets of experiments.
|Title of host publication||Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers|
|Editors||Christian Wallraven, Qingshan Liu, Hajime Nagahara|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||12|
|Publication status||Published - 2022|
|Event||6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online|
Duration: 2021 Nov 9 → 2021 Nov 12
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||6th Asian Conference on Pattern Recognition, ACPR 2021|
|Period||21/11/9 → 21/11/12|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).
© 2022, Springer Nature Switzerland AG.
- Domain generalization
- Face anti spoofing
- Meta learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)