Enhancing Person Re-identification by Integrating Gait Biometric

This paper proposes a method to enhance person re-identification by integrating gait biometric. The framework consists of the hierarchical feature extraction and matching methods. Considering the appearance feature is not discriminative in some cases, the feature in this work composes of the appearance feature and the gait feature for shape and temporal information. In order to solve the view-angle change problem and measuring similarity, metric learning to rank is adopted. In this way, data are mapped into a metric space so that distances between people can be measured accurately. Then two fusion strategies are proposed. The score-level fusion computes distances of the appearance feature and the gait feature respectively and combine them as the final distance between samples. Besides, the feature-level fusion firstly installs two types of features in series and then computes distances by the fused feature. Finally, our method is tested on CASIA gait dataset. Experiments show that gait biometric is an effective feature integrated with appearance features to enhance person re-identification.