Efficient Face Spoofing Detection With Flash

• Published in: IEEE transactions on biometrics, behavior, and identity science Vol. 3; no. 4; pp. 535 - 549
• Main Authors: Ebihara, Akinori F., Sakurai, Kazuyuki, Imaoka, Hitoshi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Artificial neural networks; Biometrics (access control); Classifiers; diffuse reflection; edge device; efficient learning; Face recognition; Face spoofing detection; Faces; small data; Specular reflection; Spoofing
• ISSN: 2637-6407; 2637-6407
• DOI: 10.1109/TBIOM.2021.3076816

In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making it suitable for resource-constrained devices such as mobile phones. Utilizing one monocular visible-light camera, the proposed algorithm takes two facial photos, taken with and without a flash, respectively. The proposed <inline-formula> <tex-math notation="LaTeX">SpecDiff </tex-math></inline-formula> descriptor is constructed by leveraging two types of reflection: (i) specular reflections from the iris region that have a specific intensity distribution depending on liveness, and (ii) diffuse reflections from the entire face region that represents the 3D structure of a subject's face. Classifiers trained with the <inline-formula> <tex-math notation="LaTeX">SpecDiff </tex-math></inline-formula> descriptor outperform other flash-based PAD algorithms on both an in-house database and four publicly available databases: NUAA, Replay-Attack, Spoofing in the Wild, and OULU-NPU. Furthermore, the proposed algorithm achieves statistically significantly better accuracy to that of an end-to-end, deep neural network classifier, while being approximately six-times faster execution speed. The limitation of the proposed algorithm is also quantified under various adversarial lighting conditions, to guide users for the safe deployment of the algorithm. The code is publicly available at https://github.com/Akinori-F-Ebihara/SpecDiff-spoofing-detector . Example images of in-house database are also available at https://github.com/Akinori-F-Ebihara/SpecDiff_in_house_database_sample .