在真实环境中使用深度残余网络进行面部情感识别

使用视觉提示自动影响识别是实现人与机器之间完全交互的重要任务。应用可以在辅导系统和人类计算机交互中找到。朝这个方向迈出的关键一步是面部特征提取。在本文中，我们提出了一个面部特征提取器模型，该模型在 RealEyes 公司提供的野生和大规模收集的视频数据集上进行了培训。数据集由一百万个标记帧和 2，616，000 个主题组成。由于时间信息对情绪识别领域很重要，我们利用 LSTM 细胞捕获数据中的时间动态。为了显示我们预先训练的面部影响建模模型的有利属性，我们使用 RECOLA 数据库，并与当前最先进的方法进行比较。我们的模型在和谐系数方面提供了最佳效果。

原文题目：Facial Emotion Recognition using Deep Residual Networks in Real-World Environments

原文：Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature extractor model trained on an in-the-wild and massively collected video dataset provided by the RealEyes company. The dataset consists of a million labelled frames and 2,616 thousand subjects. As temporal information is important to the emotion recognition domain, we utilise LSTM cells to capture the temporal dynamics in the data. To show the favourable properties of our pre-trained model on modelling facial affect, we use the RECOLA database, and compare with the current state-of-the-art approach. Our model provides the best results in terms of concordance correlation coefficient.