Gait recognition using wavelet descriptors and independent component analysis

This paper proposes an approach to automatic gait recognition based on wavelet descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, the background extraction method is applied to subtract the moving human figures accurately and to obtain binary silhouettes. Secondly, these silhouettes are described with wavelet descriptors and converted into one-dimensional signals to get the independent components (ICs) of these feature signals through ICA. Then, a fast and robust fixed-point algorithm for calculating the ICs is adopted and a selection criterion how to choose ICs is given. Lastly, the nearest neighbor and support vector machine classifiers are chosen for recognition and the method is tested on the XAUT and NLPR gait database. Experimental results show that our method has encouraging recognition accuracy with comparatively low computational cost.