An Iterative Gait Prototype Learning Algorithm Based on Tangent Distance

Being the only biometry certification techniques for remote surveillance, gait recognition, on one hand, is regarded as being of important potential value, hence a lot of algorithms have been proposed, on the other hand, it has encountered a lot of challenges. Among all of the challenges gait recognition encountered, one of them is how to extract features efficiently from a sequence of gait frames. To solve this problem, and also based on the fact that gait energy image （GEI） is effective for feature representation, an iterative prototype algorithm based on tangent distance is proposed. Firstly, it is assumed that different gaits lie in different manifolds. As a result, the proposed algorithm refines the definition of gait energy image（GEI） using tangent distance. Then an iterative algorithm is proposed to learn the prototypes by solving an optimization problem. Finally, principal component analysis （PCA） is performed on the prototypes to obtain gait features for classification. The proposed method is proved converged, and experiment results show the promising results of the proposed algorithm in accuracy compared with the GEIs. The rationality of the assumption that gaits lie in specific manifolds is also validated through experiments.