Using multiple views for gait-based gender classification

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. In this paper, we propose an approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multilinear principal component analysis is employed to reduce dimensionality of the tensor objects which integrate all views. Compared with other methods, the proposed fusion scheme shows more effective performance for multi-view gait based gender classification.