Human identification using body prior and generalized EMD
The general configuration of body is a valuable cue for human identification, which is ignored by the existing approaches. In this paper, we present an approach for human identification by using body prior and the generalized Earth Mover's Distance (EMD). The common knowledge that a pedestrian is composed of upper body and the lower one is employed as a body prior. To achieve more robust body segmentation, we pursue their boundary by inducing a logistic probability map, which is approximated based on minimizing its KL divergence to the posterior probability of the observed person image. Furthermore, we generalize EMD by assigning different weights to regions of body, which are learned through logistic regression to boost discriminative power for human identification. The experimental results show that both body prior and the generalized EMD facilitate performance on human identification.