Gait recognition using images of oriented smooth pseudo motion

This paper proposes a method of gait recognition using not only shape feature but also motion feature from silhouette image sequences. The inner silhouette motion called pseudo motion is constructed by dividing the silhouette shape into small clusters and by computing many-to-many correspondence via earth mover´s morphing framework. The raw pseudo motion, however, tends to be locally fluctuated in the spatio-temporal domain, and hence the spatio-temporal regularization is imposed to provide the smooth pseudo motion. The smooth pseudo motion image sequences are further partitioned into images with eight different orientations, and then averaged over each gait period to produce images of oriented smooth pseudo motion. Both shape and motion cues are integrated in score-level fusion framework based on linear logistic regression and the single-dimensional fused distance is returned by the learned optimal weights. The experiments with the publicly available gait database show the effectiveness of the proposed method compared with the case where the shape information is used alone.