Deep learning research has made many biometric recognition solution viable, but it requires vast training data to achieve real-world generalization. Unlike other biometric traits, such as face and ear, gait samples cannot be easily crawled from the web to form massive unconstrained datasets. As the human body has been extensively studied for different digital applications, one can rely on prior shape knowledge to overcome data scarcity. This work follows the recent trend of fitting a 3D deformable body model into gait videos using deep neural networks to obtain disentangled shape and pose representations for each frame. To enforce temporal consistency in the network, we introduce a new Linear Dynamical Systems (LDS) module and loss based on Koopman operator theory, which provides an unsupervised motion regularization for the periodic nature of gait, as well as a predictive capacity for extending gait sequences. We compare LDS to the traditional adversarial training approach and use the USF HumanID and CASIA-B datasets to show that LDS can obtain better accuracy with less training data. Finally, we also show that our 3D modeling approach is much better than other 3D gait approaches in overcoming viewpoint variation under normal, bag-carrying and clothing change conditions.

深度学习研究已经使许多生物识别解决方案变得可行，但它需要大量的训练数据才能实现现实世界的泛化。本文提出了一种使用深度神经网络将三维可变形体模型拟合到步态视频中，以获得每帧的分解形状和姿势表示，通过引入基于Koopman算子理论的线性动力系统（LDS）模块和损失函数，实现网络中的时间一致性并提供无监督的步态运动正则化方法，以及预测能力以延长步态序列。我们将LDS与传统的对抗训练方法进行比较，并使用USF HumanID和CASIA-B数据集表明，相比于传统方法，LDS在使用更少的训练数据时能够获得更高的准确性。最后，我们还证明了我们的三维建模方法在正常、背负物品和更换衣物情况下对应变视角的克服能力更强。

深度库普曼运算符约束下的3D步态识别训练需求降低