Person Re-identification by Multiple Feature Representations and Metric Learning

Person re-identification is the problem of matching pedestrian images captured from multiple cameras. Feature representation and metric designing are two critical aspects in person re-identification. In this paper, we first propose an effective Convolutional Neural Network and learn it with mixed datasets as a general deep feature extractor. Secondly, we extract the hand-crafted feature of images as a supplement, then we learn the independent distance metrics for deep feature representation and hand-crafted feature representation, respectively. Finally, we validate our method on three challenging person re-identification datasets, experimental results show the effectiveness of our approach, and we achieve the best rank-1 matching rates on all the three datasets compare with the state-of-the-art methods.