Couple metric learning based on separable criteria with its application in Cross-View gait recognition

Abstract

Gait is an important biometric feature to identify a person at a istance. However, the performance of the traditional gait recognition methods may degenerate when the viewing angle is changed. This is because the viewing angle of the probe data may not be the same as the viewing angle under whichthe gait signature database is generated. In this paper, we introduce the separable criteria into the couple metric learning (CML) method, and apply this novel method to normalize gait features from various viewing angles into a couple feature spaces. Then, the gait similarity measurement is conducted in this common feature space. We incorporate the label information into the separable criteria to improve the performance of the traditional CML method. Experiments are performed on the benchmark gait database. The results demonstrate the efficiency of our method.