Human gait conveys significant information that can be used for identity recognition and emotion recognition. Recent studies have focused more on gait identity recognition than emotion recognition and regarded these two recognition tasks as independent and unrelated. How to train a unified model to effectively recognize the identity and emotion from gait at the same time is a novel and challenging problem. In this paper, we propose a novel Attention Enhanced Temporal Graph Convolutional Network (AT-GCN) for gait-based recognition and motion prediction. Enhanced by spatial and temporal attention, the proposed model can capture discriminative features in spatial dependency and temporal dynamics. We also present a multi-task learning architecture, which can jointly learn representations for multiple tasks. It helps the emotion recognition task with limited data considerably benefit from the identity recognition task and helps the recognition tasks benefit from the auxiliary prediction task. Furthermore, we present a new dataset (EMOGAIT) that consists of 1, 440 real gaits, annotated with identity and emotion labels. Experimental results on two datasets demonstrate the effectiveness of our approach and show that our approach achieves substantial improvements over mainstream methods for identity recognition and emotion recognition.

人的步态传达了可用于身份识别和情感识别的重要信息。最近的研究更多地集中在步态识别而不是情绪识别上，并且认为这两个识别任务是独立且无关的。如何训练一个统一的模型来同时有效地从步态识别身份和情感是一个新颖而具有挑战性的问题。在本文中，我们提出了一种新颖的注意力增强时间图卷积网络（AT-GCN），用于基于步态的识别和运动预测。通过时空注意力的增强，所提出的模型可以捕获在空间依赖性和时空动力学方面的判别特征。我们还提出了一种多任务学习架构，该架构可以共同学习多个任务的表示形式。它帮助数据有限的情感识别任务从身份识别任务中受益匪浅，并帮助识别任务从辅助预测任务中受益。此外，我们提出了一个新的数据集（EMOGAIT），该数据集包含1,440个真实步态，并带有身份和情感标签。在两个数据集上的实验结果证明了我们方法的有效性，并且表明我们的方法相对于主流的身份识别和情感识别方法取得了实质性的进步。