SVM based biometric authorization system by video analysis of human gait

Biometric Systems to recognize authorized person when they enter into a surveillance area has received growing attention in modern era. In this paper human gait is used as a discriminative feature for authorization. Initially background modeling is done from a video sequence and the foreground moving objects in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal, and wavelet features are extracted and fused for training and testing the multiclass support vector machine model (SVM). The proposed system is evaluated using side view videos of Chinese National Laboratory of Pattern Recognition (NLPR) gait database and experimental results demonstrate the effectiveness of our approach.