GAIT Recognition using Hybrid Method

Identifying individuals using biometric methods has recently gained growing interest from computer vision researchers for security purposes at places like airport, banks etc. Gait recognition aims essentially to address this problem by identifying people at a distance based on the way they walk i.e., by tracking a number of feature points or gait signatures. We describe a new model-based feature extraction analysis is presented using Hough transform technique that helps to read the essential parameters used to generate gait signatures that automatically extracts and describes human gait for recognition and discrete wavelet transformation (DWT) Different gait patterns are characterized by differences in limb movement patterns, overall velocity, forces, kinetic and potential energy cycles, and changes in the contact with the surface. In literature canny edge detection was used for image pre processing but it was giving erroneous edges. To extract features correctly erroneous results are very dangerous. So before that wavelet transform is applied on captured frame and then canny edge detection is applied as wavelet transform the limitations of canny edge detection were removed by Hough transform.