Structure Sparsity for Multi-camera Gait Recognition

With the rapid development of surveillance technology, there are often several cameras in one scenario. The multi-camera usage to perform gait recognition becomes a challenge problem. This paper studies multi-camera gait recognition via structure sparsity. For the multicamera structure in the training set, we propose a structure sparsity algorithm to learn informative and discriminative sparse representations; and for the structure in the testing set, we develop a new classification criteria based on the reconstruction error of learned sparse representations. In addition, we learn a dictionary from the original gait data to further improve recognition accuracy meanwhile reduce computational cost. Experimental results show that the proposed method can efficiently deal with the multi-camera gait recognition problem and outperforms the state-of-the-art sparse representation methods.