A Correlation Based Approach to Human Gait Recognition

This paper presents a new gait signature for human gait recognition which is based on the correlation analysis of the leg motion. The motion of two legs during the human walking process is one of the most important gait determinants. This cyclic motion of the two legs is extracted by applying 2-D masks on the relevant areas of the binary images. Experimental results indicate that 2nd. order and 1-D diagonal slice of 3rd. order autocorrelations of these area signals possesses significant discrimination power to build an effective gait recognition system.