Gait Recognition Based On Body Silhouette

Human gait recognition is the process of identifying individuals by their walking manners. In the last years, as one of biometrical features, gait recognition has attracted more and more research interest. Its advantages are that it is a long distant recognition technology, noninvasive and difficult to conceal。Therefore, it can be applied to security system, Human ID management, digital surveillance and so on.. Generally, gait recognition consists of three parts: preprocessing of gait sequence, feature extraction and classification. The goal of this thesis is to explore the gait recognition technology based on body silhouettes by Fourier descriptors.Firstly, An adaptive background model is applied to extracting background in dynamic environment. At the same time the algorithms of automatic histogram thresholding segmentation and morphological operators are used to accomplish the moving person segmentation. For each image sequence, cyclic width of gait analysis is performed to extract key frames, and Fourier descriptor is utilized to describe gait contour. Then eigenspace transformation based on the traditional Principal Component Analysis is applied to time-varying distance signals derived from a sequence of silhouette images to reduce the dimensionality of the input feature space. A supervised pattern classification technique called nearest neighbor algorithm is finally performed in the lower-dimensional eigenspace for recognition. Extensive experimental results on CASIA database demonstrate that the proposed algorithm has an encouraging recognition performance with relatively lower computational cost.