Multi-perspective gait recognition based on classifier fusion

Gait recognition has been well known as a promising biometric, which is non-offensive and can identify a person from a distance. In this study, a novel ensemble learning framework for gait recognition, namely multi-perspective gait recognition based on classifier fusion is proposed. Firstly, by utilising bidirectional optical flow, a new algorithm for gait feature extraction is presented, which adaptively extracts the dynamic gait characteristics of walking persons. Secondly, two base classifiers, namely the support vector machine and the hidden Markov model, are trained using the extracted dynamic gait features and traditional gait energy images separately. Thirdly, a novel algorithm is presented for combining two types of base gait classifiers together on the decision level. Finally, the proposed framework by two experiments on the well-known CASIA and OU-ISIR gait databases is evaluated, respectively, and demonstrate the advantages of the proposed methods in comparison with others.