The inherent relations among multiple face analysis tasks, such as landmark detection, head pose estimation, gender recognition and face attribute estimation are crucial to boost the performance of each task, but have not been thoroughly explored since typically these multiple face analysis tasks are handled as separate tasks. In this paper, we propose a novel deep multi-task adversarial learning method to localize facial landmark, estimate head pose and recognize gender jointly or estimate multiple face attributes simultaneously through exploring their dependencies from both image representation-level and label-level. Specifically, the proposed method consists of a deep recognition network $\mathcal{R}$ and a discriminator $\mathcal{D}$. The deep recognition network is used to learn the shared middle-level image representation and conducts multiple face analysis tasks simultaneously. Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks from image representation-level. The discriminator is introduced to enforce the distribution of the multiple face analysis tasks to converge to that inherent in the ground-truth labels. During training, the recognizer tries to confuse the discriminator, while the discriminator competes with the recognizer through distinguishing the predicted label combination from the ground-truth one. Though adversarial learning, we explore the dependencies among multiple face analysis tasks from label-level. Experimental results on benchmark databases demonstrate the effectiveness of the proposed method for multi-task face analyses.

多个面部分析任务之间的固有关系（例如地标检测，头部姿势估计，性别识别和面部属性估计）对于提高每个任务的性能至关重要，但由于通常将这些多个面部分析任务按以下方式处理，因此尚未进行深入探讨单独的任务。在本文中，我们提出了一种新颖的深度多任务对抗学习方法，该方法可通过从图像表示级别和标签级别探索它们的依赖性，来定位面部界标，估计头部姿势并共同识别性别或同时估计多个面部属性。具体来说，所提出的方法由深度识别网络组成$\mathcal{[R}$ 和一个鉴别器 $\mathcal{d}$。深度识别网络用于学习共享的中级图像表示，并同时执行多个面部分析任务。通过多任务学习机制，识别网络从图像表示层面探索了多个面部分析任务之间的依赖关系。引入区分器以强制执行多个面部分析任务的分配，以收敛到地面真相标签中固有的分布。在训练过程中，识别器试图混淆鉴别器，而鉴别器则通过将预测的标签组合与实际标签组合区分开来与识别器竞争。通过对抗性学习，我们从标签级别探索了多个面部分析任务之间的依赖性。