Gait Representation and Recognition Using Haar Wavelet and Radon Transform

This paper presented a new gait identification and authentication method based on Haar wavelet and Radon transform. This method consists of two stages, gait modeling and recognition. In the first stage, images extracted from video sequences are pre-processed into binary silhouette. In terms of gait cycle, they are divided into 4 states, in each of which the distinct images are selected. The horizontal and vertical features are acquired by Haar wavelet, and then feature vectors are obtained respectively by Radon transform. In the second stage, probe sequences are fed. After feature transform of image sequence, the value of similarity can be obtained by comparing probe vectors with gallery ones and optimized to give gait recognition. Consequently, we can improve the rate of recognition by further optimization.