Novel gait recognition technique based on SVM fusion of PCA-processed contour projection and skeleton model features

Gait is a potential behavioral feature, and many allied studies have demonstrated that it can be served as a useful biometric feature for recognition. This paper described a novel gait recognition technique based on support vector machine fusion of contour projection and skeleton model features. A principal component analysis method was used to lower the dimension of contour projection after segmenting silhouettes from the background in the key frame of gait picture sequence and a skeleton model was built to produce other shape features. The combining features were fused by a support vector machine and tested on the CASIA database at the feature level and decision level based on posterior probability. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.