Uncooperative Gait Recognition Using Joint Bayesian

Chao Li, Kan Qiao, Xin Min, Xiaoyan Pang, Shouqian Sun

Abstract: Human gait, as a soft biometric, helps to recognize people by walking without subject cooperation. In this paper, we propose a more challenging uncooperative setting under which views of the gallery and probe are both unknown and mixed up (uncooperative setting). Joint Bayesian is adopted to model the view variance. We conduct experiments to evaluate the effectiveness of Joint Bayesian under the proposed uncooperative setting on OU-ISIR Large Population Dataset (OULP) and CASIA-B Dataset (CASIA-B). As a result, we confirm that Joint Bayesian significantly outperform the state-of-the-art methods for both identification and verification tasks even when the training subjects are different from the test subjects. For further comparison, the uncooperative protocol, experimental results, learning models, and test codes are available.