A Robust Approach to Multiple Views Gait Recognition Based on Motion Contours Analysis

Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper proposes new multi-view gait recognition based on motion contour (MVGRMC) approach which tackles the problems mentioned above. Being different from other approaches of same category, The reliable extraction of gait features from images sequences and their recognition are two important issues in gait recognition. This paper proposes an automatic gait recognition approach for analyzing and classifying human gait by computer vision techniques. This approach attempts to incorporate knowledge of the static and dynamics of human gait into the feature extraction process. Our approach provides an efficient way to extract, to model shape-motion information of gait sequence, and to measure the difference between gait sequence models which is robust to gait cycle localization, gross appearance variation, and time scaling. The proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods. These results are illustrated with practical examples on popular gait databases.