상세정보
학술지
One-Class Learning Method Based on Live Correlation Loss for Face Anti-Spoofing
- 발행일
- 202011
- 출처
- IEEE Access, v.8, pp.201635-201648
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 20HR3100, 고신뢰 지능정보 서비스에서 휴먼(H)-인프라(I)-서비스(S)를 연결하는 Portal Device 보안 기술 개발, 조상래
- 초록
- As biometric authentication systems are popularly used in various mobile devices, e.g., smart-phones and tablets, face anti-spoofing methods have been actively developed for the high-level security. However, most previous approaches still suffer from diverse types of spoofing attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method for face anti-spoofing is proposed based on one-class (i.e., live face only) learning with the live correlation loss. Specifically, encoder-decoder networks are firstly trained with only live faces to extract latent features, which have an ability to compactly represent various live facial properties in the embedding space and produce the spoofing cues, which are simply obtained by subtracting the original RGB image and the generated one. After that, such features are fed into the proposed feature correlation network (FCN) so that weights of FCN learn to compute ?섃?쁫iveness?쇺?? of given features under the guidance of the live correlation loss. It is noteworthy that the proposed method only requires live facial images for training the model, which are easier to obtain than fake ones, and thus the generality power for resolving the problem of face anti-spoofing can be expected to be improved. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method.
- KSP 제안 키워드
- Authentication System, Benchmark datasets, Biometric authentication, Correlation loss, Correlation network, Embedding space, Encoder and Decoder, Face Anti-Spoofing, Facial image, Feature correlation, Learning methods