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Human Micro-Expressions in Multimodal Social Behavioral Biometrics.

Zaman Wahid1, A S M Hossain Bari1, Marina Gavrilova1

  • 1Biometric Technologies Laboratory, Department of Computer Science, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada.

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|October 14, 2023
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Summary
This summary is machine-generated.

This study introduces a new multimodal Social Behavioral Biometrics (SBB) system. Integrating human micro-expressions with other SBB traits significantly improves online user identification accuracy and recall.

Keywords:
biometric identificationhuman micro-expressionnatural language processingperson identificationsocial behavioral biometrics

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Human-Computer Interaction

Background:

  • Social Behavioral Biometrics (SBB) are crucial for online person identification.
  • Understanding unique social interaction and communication patterns is key.
  • Existing SBB systems can be enhanced with novel biometric traits.

Purpose of the Study:

  • To introduce a novel multimodal SBB system.
  • To integrate human micro-expressions from text as a new biometric trait.
  • To enhance online user identification performance.

Main Methods:

  • Developed a multimodal SBB system incorporating human micro-expressions.
  • Extracted six SBB traits, including micro-expressions, for comprehensive user representation.
  • Employed rank-level fusion using weighted Borda count to combine trait scores.

Main Results:

  • The proposed method achieved 73.87% accuracy and 74% recall on a Twitter user dataset.
  • Incorporating human micro-expressions substantially boosted identification performance.
  • Outperformed existing state-of-the-art SBB systems.

Conclusions:

  • Human micro-expressions are a valuable biometric trait for SBB.
  • Multimodal SBB systems integrating diverse traits offer superior performance.
  • The proposed fusion method effectively enhances online user identification.