Gait Recognition via Independent Component Analysis Based on Support Vector Machine and Neural Network

This paper proposes a method of automatic gait recognition using Fourier descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, a simple background generation algorithm is introduced to subtract the moving figures accurately and to obtain binary human silhouettes. Secondly, these silhouettes are described with Fourier descriptors and converted into associated one-dimension signals. Then ICA is applied to get the independent components of the signals. For reducing the computational cost, a fast and robust fixed-point algorithm for calculating ICs is adopted and a criterion how to select ICs is put forward. Lastly, the nearest neighbor (NN), support vector machine (SVM) and backpropagation neural network (BPNN) classifiers are chosen for recognition and this method is tested on the small UMD gait database and the NLPR gait database. Experimental results show that our method has encouraging recognition accuracy.