Gait recognition using dynamic time warping

We propose a methodology for gait recognition based on dynamic time warping. The gait sequences are initially partitioned into gait cycles and then the test cycles are compared to reference cycles using dynamic time warping. The final distance between a test and a reference sequence is determined using a nonlinear rule. Experimental results are reported showing an improvement in recognition performance in comparison to the baseline algorithm on the "gait challenge" database.