Personal Recognition Using Multi-angles Gait Sequences

This paper presents an automatic gait recognition system which recognizes a person by the way he/she walks. The gait signature is obtained based on the contour width information of the silhouette of a person. Using this statistical shape information, we could capture the compact structural and dynamic features of the walking pattern. As the extracted contour width feature is large in size, Fisher Discriminant Analysis is used to reduce the dimension of the feature set. After that, a modified Probabilistic Neural Networks is deployed to classify the reduced feature set. Satisfactory result could be achieved when we fused gait images from multiple viewing angles. In this research, we aim to identify the complete gait cycle of each subjects. Every person walks at difference paces and thus different numbers of frame sizes are required to record the walking pattern. As such, it is not robust and feasible if we take a fixed number of video frames to process the gait sequences for all subjects. We endeavor to find an efficient method to identify the complete gait cycle of each individual. In this case, we could work on succinct representation of the gait pattern which is invariant to walking speed for each individual.