This paper proposes a novel cross-domain transfer networks (CDTN) that is employed for multi-view gait recognition. CDTN consists of two VGAN layers and a GAN unit. The VGAN layer merges variational autoencoder (VAE) into generative adversarial network (GAN) by replacing the generator of GAN with VAE. Gait energy images (GEIs) are taken as the input of the VGAN layer, and then discriminative loss, reconstruction loss and Kullback–Leibler divergence are adopted to optimize CDTN synchronously. Two VGAN layers are connected by a GAN unit that takes the two gait samples collected under different views as input. After optimization, the output of generator is decoded by a decoder, the decoding results are taken as the output of CDTN. Finally, extensive experiments are conducted on two famous gait datasets. Compare with the state-of-the-art methods, CDTN achieves better gait recognition accuracies, such as 94.78% under single view angle and 93.68% under multi-view angles, which outperforms the existing methods by a significant margin. The results indicate that CDTN is effective for improving the accuracy of multi-view gait recognition. Besides, CDTN provides an important reference for solving the similar problems.

本文提出了一种新颖的跨域传输网络（CDTN），该网络用于多视图步态识别。CDTN由两个VGAN层和一个GAN单元组成。VGAN层通过用VAE代替GAN的生成器，将变分自编码器（VAE）合并到生成对抗网络（GAN）中。步态能量图像（GEI）被用作VGAN层的输入，然后采用判别损失，重构损失和Kullback-Leibler散度来同步优化CDTN。两个VGAN层由GAN单元连接，该GAN单元将在不同视图下收集的两个步态样本作为输入。经过优化后，发生器的输出由解码器解码，解码结果作为CDTN的输出。最后，对两个著名的步态数据集进行了广泛的实验。与最先进的方法相比，CDTN具有更好的步态识别准确度，例如在单视角下为94.78％，在多视角下为93.68％，大大优于现有方法。结果表明，CDTN对于提高多视角步态识别的准确性是有效的。此外，CDTN为解决类似问题提供了重要参考。