Despite the rapid increase of research on individual identification in recent years, most of them focused on extracting the invariant visual cues of individuals. However, few efforts have been devoted to exploring the inherent feature caused by physiological differences. The main challenge in this task arises from two aspects: (i) the individual inherent feature (style) of each pose may be unique; (ii) the individual inherent feature and the shared feature from pose are always coupled together. In this paper, we propose a novel model, namely Disentangling Style Network (DS-Net), which contains an alignment module to match similar poses from different individuals, and a disentangling module to encode the individual inherent feature and shared feature separately. Theoretically, the sub-modules in our model are mutually beneficial for each other, the alignment module can improve the quality of training poses pairs in the disentangling module, meanwhile, the shared feature encoded by the disentangling module can also improve the accuracy of the alignment module. The coupled network contains a set of functional components, can be efficiently trained by gradient-based optimizers in a practical iterative way. We evaluate the proposed DS-Net on several public databases, matching or outperforming the state-of-the-art approaches. The code is publicly available¹ .

尽管近年来有关个体识别的研究迅速增加，但大多数研究集中在提取个体的不变视觉线索上。然而，很少有努力致力于探索由生理差异引起的固有特征。此任务的主要挑战来自两个方面：（i）每个姿势的个体固有特征（风格）可能是独特的；（ii）个体固有特征和姿势共享特征始终耦合在一起。在本文中，我们提出了一种新颖的模型，即解缠结样式网络（DS-Net），该模型包含一个对齐模块以匹配来自不同个体的相似姿势，以及一个解缠结模块以分别编码各个固有特征和共享特征。从理论上讲，我们模型中的子模块是互惠互利的，对齐模块可以提高解纠缠模块中训练姿势对的质量，同时，解纠缠模块编码的共享特征也可以提高对齐模块的准确性。耦合网络包含一组功能组件，可以由基于梯度的优化器以实际的迭代方式有效地进行训练。我们在几个公共数据库上评估拟议的DS-Net，以匹配或优于最新方法。该代码是公开可用的 可以由基于梯度的优化器以实际的迭代方式有效地进行训练。我们在几个公共数据库上评估拟议的DS-Net，以匹配或优于最新方法。该代码是公开可用的 可以由基于梯度的优化器以实际的迭代方式有效地进行训练。我们在几个公共数据库上评估拟议的DS-Net，以匹配或优于最新方法。该代码是公开可用的¹。