Automatic Gait Recognition using Dynamic Variance Features

Human gait recognition is currently one of the most active research topics in computer vision. Existing recognition methods suffer, in our opinion, from two shortcomings: either much expensive computation or poor identification effect; thus a new method is proposed to overcome these shortcomings. Firstly, we detect the binary silhouette of a walking person in each of the monocular image sequences. Then, we extract the pixel values at the same pixel position over one gait cycle to form a dynamic variation signal (DVS). Next, the variance features of all the DVS are computed respectively and a matrix is constructed to describe the dynamic gait signature of individual. Finally, the correlation coefficient measure based on the gait cycles and two different classification methods (NN and KNN) are used to recognize different subjects. Experimental results show that our method is not only computing efficient, but also very effective of correct recognition rates over 90% on both UCSD and CMU databases