论文标题
基于选择性特征共享的面部表达识别和综合的深度多任务学习
Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing
论文作者
论文摘要
多任务学习是用于深度学习面部表达识别任务的有效学习策略。但是，在不同任务之间传输信息时，大多数现有方法都会有限考虑功能选择，这可能会导致训练多任务网络时的任务干扰。为了解决这个问题，我们提出了一种新颖的选择性特征共享方法，并建立了一个多任务网络，用于面部表达识别和面部表达综合。所提出的方法可以有效地在不同任务之间传递有益的特征，同时滤除无用和有害信息。此外，我们采用面部表达综合任务来扩大和平衡训练数据集，以进一步增强所提出方法的概括能力。实验结果表明，所提出的方法在那些常用的面部表达识别基准上实现了最先进的性能，这使其成为现实世界面部表达识别问题的潜在解决方案。
Multi-task learning is an effective learning strategy for deep-learning-based facial expression recognition tasks. However, most existing methods take into limited consideration the feature selection, when transferring information between different tasks, which may lead to task interference when training the multi-task networks. To address this problem, we propose a novel selective feature-sharing method, and establish a multi-task network for facial expression recognition and facial expression synthesis. The proposed method can effectively transfer beneficial features between different tasks, while filtering out useless and harmful information. Moreover, we employ the facial expression synthesis task to enlarge and balance the training dataset to further enhance the generalization ability of the proposed method. Experimental results show that the proposed method achieves state-of-the-art performance on those commonly used facial expression recognition benchmarks, which makes it a potential solution to real-world facial expression recognition problems.
