Gait Recognition Based on Human Leg Gesture Classification

This paper presents a human gait recognition system based on a leg gesture separation. Main innovation in this paper includes gait recognition using leg gesture classification which gives a high precision recognition system. Five state of leg in human gait are extracted after background estimation and human detection in the scene. Leg gestures are classified over directional chain code of bottom part of silhouette contour. A spatio-temporal data base namely Energy Halation Image (EHI) is constructed over bottom part of human silhouette from train film sequence for five leg gestures separately. Eigen space of energy halation is applied to multilayer perceptron neural network. Five neural network system recognize people but with medium recognition rate. A neuro-fuzzy fusion technique is used for obtaining high recognition rate. Experimental results is performed over a suitable data base include 20 samples for eight person which each sample have 100 frames approximately. 99% recognition rate of the proposed system is obtained over 10 samples test patterns.

کلیدواژه ها:

Human leg gesture separation ، Gait recognition ، Background estimation ، Spatiotemporal data base ، Neural network classifier ، Neuro-fuzzy based classifier fusion