Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion

This paper proposes a gait recognition method using multiple gait features representations based on independent component analysis (ICA) and genetic fuzzy support vector machine (GFSVM) for the purpose of human identification at a distance. Firstly, the moving human figures are subtracted using simple background modeling to obtain binary silhouettes. Secondly, these silhouettes are characterized with three kinds of gait representations including Fourier descriptor, wavelet descriptor and pseudo-Zernike moment. Then, ICA and GFSVM classifier are chosen for recognition and the method is tested on two gait databases. Comparative performance between these feature representations is investigated and better performance has been achieved than either one individually. Meanwhile, one multiple views fusion recognition approach on the decision level based on product of sum (POS) rule is introduced to overcome the limitation of most single view recognition methods, which achieves better performance than the traditional rank-based fusion rules. Experimental results show that our method has encouraging recognition accuracy.