Gait recognition systems have shown great potentials in the field of biometric recognition. Unfortunately, the accuracy of gait recognition is easily affected by a large view angle. To address the problem, this study proposes a feature distribution consistent generative adversarial network (FDC-GAN) to transform gait images from arbitrary views to the target view and then perform identity recognition. Besides reconstruction loss, view classification and identity preserving loss are also introduced to guide the generator to produce gait images of the target views and keep identity information simultaneously. To further encourage the network to generate gait images whose feature distribution can well align the true distribution, we also exploit the recently proposed recurrent cycle consistency loss, which can help to remove the unnoticed and useless content preserved in the generated gait images. The experimental results on datasets CASIA-B and OU-MVLP demonstrate the state-of-the-art performance of our model compared to other GAN-based cross-view gait recognition models.

步态识别系统在生物特征识别领域显示出巨大的潜力。不幸的是，步态识别的准确性很容易受到大视角的影响。为了解决这个问题，本研究提出了一种特征分布一致的生成对抗网络（FDC-GAN）来将步态图像从任意视图转换为目标视图，然后进行身份识别。除了重建损失之外，还引入了视图分类和身份保留损失来指导生成器生成目标视图的步态图像并同时保留身份信息。为了进一步鼓励网络生成特征分布可以很好地对齐真实分布的步态图像，我们还利用了最近提出的循环一致性损失，这有助于去除生成的步态图像中保留的未被注意和无用的内容。与其他基于 GAN 的交叉视图步态识别模型相比，数据集 CASIA-B 和 OU-MVLP 上的实验结果证明了我们模型的最新性能。