Gait recognition system using decision-level fusion

Gait recognition has recently attracted increasing interest from the biometric society. In this paper, we present a gait recognition system based on the fusion of multiple gait cycles using a new gait representation. First, a gait sequence is automatically partitioned into multiple gait cycles by finding the local minima of width signal. After gait cycle partitioning, we extract a new gait feature called motion contour image (MCI) that captures the contour of the binary silhouette image of a walking individual. Finally, for human identification, the outputs of nearest neighbor classifiers are fused at a decision level based on majority voting. Our proposed system is tested on the CASIA gait dataset A. Experimental results show that the proposed system is better than or equal to previous works in terms of correct classification rate.