Study On Gait-based Human Identification

With the development of the security requirement of modern society, it becomes the mainstream to identify one's identity with biometric today. Among the different biometrical character, gait has its own advantages which is untouched and requires little about the quality of the original data, such as image resolution. The former advantage makes the gait's input to be non body-invading, and the latter broaden its applications. Furthermore, gait is difficult to be disguised. All of these lead the gait recognition becomes the second-generation biometric recognition technology based on vision movement. Also, many researches have been deployed on this field.Working on gait recognition methods, this dissertation concentrates on the following topics:(1) The status-art of gait and some background knowledge of principal curves are introduced. Considering kinds of detection methods, the background subtraction is used in detection. On the other hand, principal curves can reflect the inherent structure of the data and describe nonlinear data, which is beneficial to the contour extraction.(2) The gait representation based on moment is discussed. A novel gait representation method based on the feature of image moment is presented in the paper. we extract Hu moments and Wavelet moments for gait static representation, and fuse the width information of gait as recognition clues, in order to make up for a single characteristic which brought about the problem of low recognition rate.(3) In gait recognition stage, a gait recognition algorithm based on artificial immune model is proposed. Because of the particularity of gait recognition, we design an immune model which can work effectively based on theory analysis and experiments on CASIA base using the selected characteristics of this paper. The experiment result shows that the algorithm is effective and we have got higher recognition rate.