지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.9 No.3
- 발행연도
- 2020.6
- 수록면
- 203 - 211 (9page)
- DOI
- 10.5573/IEIESPC.2020.9.3.203
이용수
초록· 키워드
오류제보하기
Face recognition has been adopted widely for real-world applications because of its convenience and contactless nature. On the other hand, forged faces for spoofing attacks can be fabricated easily using a variety of materials, such as pictures, high-resolution videos, and printed masks, etc., which pose a great threat to face-based recognition systems. Therefore, face antispoofing has become an essential technique to achieve high-level security. Although many studies have explored effective features to discriminate live faces from fake ones, even with deep neural networks, they still struggle to grasp meaningful differences from a single image because of the sophisticated spoofing attacks with various media. This paper proposes a novel method for face anti-spoofing based on stereo facial images. Because the three-dimensional structure of a live face clearly yields a structural difference in the image pair taken by a stereo camera, whereas significant differences do not occur in fake faces of two-dimensional planes, this paper proposes to learn the differences of left-right image pairs in the latent space of a deep neural network. One important advantage of the proposed method is that the structural difference is encoded implicitly in a nonlinear manner through the deep architecture without explicitly computing the disparity. The experimental results on a constructed dataset revealed the proposed method to be effective for diverse spoofing attacks.
목차
Abstract
1. Introduction
2. Related Work
3. Proposed Method
4. Experimental Results
5. Conclusion
References
참고문헌 (16)
참고문헌 신청
T. Ojala / 2002 / Multiresolution gray-scale and rotation invariant texture classification with local binary patterns / IEEE Trans. Pattern Anal. Mach. Intell 24(7):971~987
Y. Atoum / 2017 / Face anti-spoofing using patch and depth-based CNNs / Proc. IEEE Int. J. Conf. Bio :319~328
X. Sun / 2016 / Dual camera based feature for face spoofing detection / Proc. Chi. Conf. Pattern Recognit :332~344
J. Maatta / 2011 / Face spoofing detection from single images using microtexture analysis / Proc. IEEE Int. Joint Conf. Biometrics :1~7
I. Chingovska / 2012 / On the effectiveness of local binary patterns in face antispoofing / Proc. IEEE Int. Conf. Biometrics Speial Interest Group :1~7
Y. Atoum / 2017 / Face anti-spoofing using patch and depth-based CNNs / Proc. IEEE Int. J. Conf. Bio :319~328
X. Sun / 2016 / Dual camera based feature for face spoofing detection / Proc. Chi. Conf. Pattern Recognit :332~344
J. Maatta / 2011 / Face spoofing detection from single images using microtexture analysis / Proc. IEEE Int. Joint Conf. Biometrics :1~7
I. Chingovska / 2012 / On the effectiveness of local binary patterns in face antispoofing / Proc. IEEE Int. Conf. Biometrics Speial Interest Group :1~7
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