Engineering Hand-designed and Deeply-learned features for person Re-identification

Abstract

Person re-identification is still a useful and challenging task in pattern recognition and computer vision. Based on a probe image, the task focuses on identifying a set of matching images from a gallery set. In this work, a novel multi-feature fusion-based person re-identification framework is proposed. The key technique is to intelligently fuze hand-crafted feature and deeply-learned feature. More specifically, hand-crafted features from both local and global region are extracted from each image. Afterward, a novel Convolutional Neural Networks (CNN) is trained based on combining the three datasets, wherein robust deep features can be obtained. Finally, the above hand-crafted feature as well as the deep features are optimally fuzed for calculating a robust re-identification result. Extensive experimental results on three state-of-the-art data sets have demonstrated the effectiveness of our method.