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3D Visual passcode: Speech-driven 3D facial dynamics for behaviometrics
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DOI：10.1016/j.sigpro.2019.02.025.png)
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Abstract
En 中文
Face biometrics have achieved remarkable performance over the past decades, but unexpected spoofing of the static faces poses a threat to information security. There is an increasing demand for stable and discriminative biological modalities which are hard to be mimicked and deceived. Speech-driven 3D facial motion is a distinctive and measurable behavior-signature that is promising for biometrics. In this paper, we propose a novel 3D behaviometrics framework based on a 3D visual passcode derived from speech-driven 3D facial dynamics. The 3D facial dynamics are jointly represented by 3D-keypoint-based measurements and 3D shape patch features, extracted from both static and speech-driven dynamic regions. An ensemble of subject-specific classifiers are then trained over selected discriminative features, which allows for a discriminant speech-driven 3D facial dynamics representation. We construct the first publicly available Speech-driven 3D Facial Motion dataset (S3DFM) that includes 2D-3D face video plus audio samples from 77 participants. The experimental results on the S3DFM show that the proposed pipeline achieves a face identification rate of 96.1%. Detailed discussions are presented, concerning anti-spoofing, head pose variation, video frame rate, and applicability cases. We also give comparison with other baselines on deep and shallow 2D face features. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Behaviometrics
Dynamic face recognition
Lip motion
Speech-driven
3D Face database
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