Cross-view gait recognition through ensemble learning

Gait has been well known as an unobtrusive promising biometric to identify a person from a distance. However, the effectiveness of silhouette-based approaches in gait recognition is diluted due to variations of view angles. In this paper, we put forward a novel and effective method of gait recognition: cross-view gait recognition based on ensemble learning. The proposed method greatly enhances the effectiveness and reduces the sensitivity of gait recognition under various view angles conditions. Furthermore, in this paper we will introduce a novel algorithm based on ensemble learning for combining several gait learners together, which utilizes a well-designed gait feature based on area average distance. Through experimental evaluations on the well-known CASIA gait database and OU-ISIR gait database, our paper demonstrates the advantages of the proposed method in comparison with others. The contribution of this research work is to resolve the multiview angles problem of gait recognition through assembling several gait learners.