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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 …