Cross-view gait recognition based on human walking trajectory

• 3-D gravity center track can be constructed by single-view gait sequence.
• View variance can be realized without gaps based on projection principle.
• View can be calculated directly without classification and cluster.
• Viewing angle labeled in current gait database can be taken as cubic view.

We propose in this paper a novel cross-view gait recognition method based on projection of gravity center trajectory (GCT). We project the coefficients of 3-D GCT in reality to different view planes to complete view variation. Firstly, we estimate the real GCT curve in 3-D space under different views by statistics of limb parameters. Then, we get the view transformation matrix based on the projection principle between curve and plane, and estimate the view of a silhouette sequence by this matrix to complete view variance of gait features. We calculate the body part trajectory on silhouette sequence to improve recognition accuracy by using correlation strength as similarity measure. Lastly, we take nested match method to calculate the final matching score of two kinds of features. Experimental results on the widely used CASIA-B gait database demonstrate the effectiveness and practicability of the proposed method.