Face alignment has been applied widely in the field of computer vision, which is still a very challenging task for the existence of large pose, partial occlusion, and illumination, etc. The method based on deep regression neural network has achieved the most advanced performance in the field of face alignment in recent years, and how to learn more representative facial appearance is the key to face alignment. Based on the idea of Multi-task Learning, we propose a Multi-task Adversarial Autoencoder (MTAAE) network, which can learn more representative facial appearance for heatmap regression and improve the performance of face alignment in the wild. MTAAE is composed of three tasks. The main task uses the heatmap regression method to locate the position of landmarks and introduces a discriminator on the landmark heatmaps to generate more realistic heatmaps. Facial attribute estimation tasks and face reconstruction task based on Adversarial Autoencoder respectively extract discriminative and generative representations to improve the effect of heatmap regression. At the same time, the dynamic weight network is designed to assign a weight coefficient dynamically and reasonably for each auxiliary task. Extensive experiments on 300W, MTFL, and WFLW datasets demonstrate that our method is more robust in complex environments and outperforms state-of-the-art methods.

人脸对齐技术已经在计算机视觉领域得到了广泛的应用，对于大姿态，部分遮挡和照明等问题的存在，仍然是一项非常具有挑战性的任务。基于深度回归神经网络的方法取得了最先进的性能。近年来，人脸对齐领域以及如何学习更具代表性的人脸外观是人脸对齐的关键。基于多任务学习的思想，我们提出了一种多任务对抗自动编码器（MTAAE）网络，该网络可以学习更多具有代表性的面部表情以进行热图回归并提高野外面部对齐的性能。MTAAE由三个任务组成。主要任务使用热图回归方法来定位地标的位置，并在地标热图上引入鉴别器以生成更逼真的热图。基于对抗自动编码器的面部属性估计任务和面部重建任务分别提取判别式和生成式表示，以提高热图回归的效果。同时，动态权重网络旨在为每个辅助任务动态合理分配权重系数。在300W，MTFL和WFLW数据集上进行的大量实验表明，我们的方法在复杂的环境中更强大，并且性能优于最新方法。基于对抗自动编码器的面部属性估计任务和面部重建任务分别提取判别式和生成式表示，以提高热图回归的效果。同时，动态权重网络旨在为每个辅助任务动态合理分配权重系数。在300W，MTFL和WFLW数据集上进行的大量实验表明，我们的方法在复杂的环境中更加健壮，并且性能优于最新方法。基于对抗自动编码器的面部属性估计任务和面部重建任务分别提取判别式和生成式表示，以提高热图回归的效果。同时，动态权重网络旨在为每个辅助任务动态合理分配权重系数。在300W，MTFL和WFLW数据集上进行的大量实验表明，我们的方法在复杂的环境中更强大，并且性能优于最新方法。