Integration of multiple soft biometrics for human identification

• A new local feature descriptor is proposed for facial shape representation.
• The performance on still and surveillance face datasets is comparable to the state of the arts.
• We present experimental findings on integration of face and body based soft biometrics.
• Five score fusion techniques are examined to determine the most reliable method.
• Fuzzy logic is discovered as the most effective score fusion.

We propose a computational approach to human identification based on the integration of face and body related soft biometric traits. In previous studies on soft biometrics, several methods for human identification using semantic descriptions have been introduced. Though the results attained exhibit the effectiveness of such techniques in image retrieval and short term tracking of subjects, semantics literally limits the ability of a biometric system to provide conclusive identification. This paper presents a new framework for biometric identification based solely on multiple measured soft biometric traits. The paper describes techniques for extracting/estimating face and body based soft biometric traits from frame set. Furthermore, we utilized a sequential attribute combination method to perform attribute selection prior to integration at match score level. Finally, an evaluation of five score fusion techniques is performed. The results show that the proposed framework can be utilized to model an adequate soft biometric system with rank-1 identification rate of 88%.

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