基于CNN 和 DLTL 的步态虚拟样本生成方法.

To solve the problem of small sample of gait recognition in the field of counterterrorism and security issues, this paper proposed a novel gait virtual sample generation method based on deep CNN and DLTL. Firstly, it extracted gait style feature map based on low-level of CNN model VGG19, and then it used the DL to carry on the style feature training. Thus it made style feature model. Moreover, high-level of VGG19 extracted gait context feature map, and then it used the TL to make context feature map carry on the style characteristic learning. Finally, it obtained the virtual migration samples. Experimental results demonstrate that these virtual samples remain individual gait feature but style feature. So this method can effectively expand small sample size. At the same time, when the number of virtual samples increase to a certain number, gait recognition rate has improved . Compared with the existing virtual sample generation method, the method has a better performance, which can generate virtual samples in large numbers and improve the recognition rate of gait recognition steadily. [ABSTRACT FROM AUTHOR]