Human Micro-Expression: A Novel Social Behavioral Biometric for Person Identification

Abstract

The reliance on Online Social Networks (OSN) for both formal and informal social interactions has dramatically changed the way people communicate. In this paper, a novel Social Behavioral Biometric (SBB), human micro-expression, is introduced for person identification. An emotion detection model is developed to extract emotion probability scores from person’s writing samples posted on Twitter. The corresponding emotion-progression features are extracted using an original technique that turns users’ microblogs into emotion-progression signals. Finally, a novel social behavioral biometric system that leverages rank-level weighted majority voting to achieve an accurate person identification is implemented. The proposed system is validated on a proprietary benchmark dataset consisting of 250 Twitter users. The experimental results convincingly demonstrate that the proposed social behavioral biometric, human micro-expression, possesses a strong distinguishable ability and can be used for person identification. The study further reveals that the proposed social behavioral biometric outperforms all the original SBB traits.

Keywords

• Biometric security
• emotion extraction
• emotion progression signals
• human micro-expression
• natural language processing
• person identification