期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 12, 页码 4726-4740出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2466088
关键词
Face spoofing attack detection; mobile device; face biometric system; spectral analysis; visual codebook; time-spectral visual features
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior through DeepEyes Project
- Fundacao de Amparo a Pesquisa do estado de Minas Gerais [APQ-00567-14, APQ-01806-13]
- Microsoft Research
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo [2010/05647-4, 2011/22749-8]
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [304352/2012-8, 307113/2012-4, 477457/2013-4, 477662/2013-7, 487529/2013-8]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [10/05647-4] Funding Source: FAPESP
Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.
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