Multi-view joint learning network for pedestrian gender classification

Abstract

Since the object occlusion, view variation, arbitrary shape of pedestrian, etc., are inherited in the pedestrian images, the pedestrian gender classification is an extremely challenging task in the object recognition field. To address this problem, an effective multi-view joint learning network (MJLN) is proposed for pedestrian gender classification. Based on the observation that the view variation of pedestrian will affect the judgment of pedestrian gender, the proposed MJLN simultaneously performs pedestrian gender learning and pedestrian view learning. Consequently, the determination of pedestrian view (i.e., frontal, rear, and left/right profile) obtained by the pedestrian view learning module will effectively assist the pedestrian gender learning module to yield a more robust and distinguishable feature so as to improve the gender classification performance. Extensive experiments on multiple challenging pedestrian datasets, which includes CUHK, VIPeR, GRID, PRID, and MIT, have demonstrated that the proposed MJLN can effectively promote the gender classification performance, and outperforms multiple state-of-the-art methods.