Person Re-identification Using Appearance Classification

In this paper, we present a person re-identification method based on appearance classification. It consists a human silhouette comparison by characterizing and classification of a persons appearance (the frontal and the back appearance) using the geometric distance between the detected head of person and the camera. The combination of the head detector, the orthogonal iteration algorithm to help head pose estimation and appearance classification is the novelty of our work. In this way, robustness against viewpoint, illumination and clothes appearance changes is achieved. Our approach uses matching of interest-points descriptors based on fast cross-bin metric. The approach applies to situations where the number of people varies continuously, considering multiple images for each individual.