论文标题
迈向一般的深度提取器，以识别面部表达
Towards a General Deep Feature Extractor for Facial Expression Recognition
论文作者
论文摘要
人脸传达了大量信息。通过面部表情，面部能够传达许多情感而无需口头表达。视觉情绪识别已被广泛研究。最近，为此任务提出了一些端到端训练的深神经网络。但是，这样的模型通常缺乏整个数据集的概括能力。在本文中，我们提出了深层表达矢量提取器（DeepFever），这是一种新的基于深度学习的方法，它可以学习一种视觉特征提取器，足以将其应用于任何其他面部情感识别任务或数据集。 DeepFever的表现优于AffectNet和Google面部表达比较数据集的最先进的结果。 DeepFever的提取功能还可以很好地推广到其他数据集（即使在培训中看不见的数据集），即现实世界情感面孔（RAF）数据集。
The human face conveys a significant amount of information. Through facial expressions, the face is able to communicate numerous sentiments without the need for verbalisation. Visual emotion recognition has been extensively studied. Recently several end-to-end trained deep neural networks have been proposed for this task. However, such models often lack generalisation ability across datasets. In this paper, we propose the Deep Facial Expression Vector ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual feature extractor general enough to be applied to any other facial emotion recognition task or dataset. DeepFEVER outperforms state-of-the-art results on the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's extracted features also generalise extremely well to other datasets -- even those unseen during training -- namely, the Real-World Affective Faces (RAF) dataset.
