Improving person recognition by weight adaptation of soft biometrics

One of the main challenges of current person recognition techniques lies on difficulties of recognition in various poses. Recently, attention has been focused on using soft biometric information extracted from the human body to overcome the biometric recognition system's limitation in unconstrained environments. In this paper, we integrate the face and body information in a linear combination. We propose a novel approach in which the weights of features in the recognition system are adapted based on the reliability of the detected joints extracted from the body and the correlation between features. We evaluate the proposed approach in recognizing a five person group in various poses such as sitting and circular walking. The method was applied to a service robot equipped with the Kinect sensor. The results show a mean improvement of 4.39% after weight adaptation based on the correlation between features and 6.88% after consideration of the reliability of the features.