Facial emotion recognition (FER) has been extensively researched over the past two decades due to its direct impact in the computer vision and affective robotics fields. However, the available datasets to train these models include often miss-labelled data due to the labellers bias that drives themodel to learn incorrect features. In this paper, a facial emotion recognition system is proposed, addressing automatic face detection and facial expression recognition separately, the latter is performed by a set of only four deep convolutional neural network respect to an ensembling approach, while a label smoothing technique is applied to deal with the miss-labelled training data. The proposed system takes only 13.48 ms using a dedicated graphics processing unit (GPU) and 141.97 ms using a CPU to recognize facial emotions and reaches the current state-of-the-art performances regarding the challenging databases, FER2013, SFEW 2.0, and ExpW, giving recognition accuracies of 72.72%, 51.97%, and 71.82% respectively.

中文翻译：

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使用平滑深度神经网络集成的实时面部表情识别

过去二十年来，由于面部表情识别（FER）直接影响计算机视觉和情感机器人领域，因此对其进行了广泛的研究。但是，由于标签驱动程序的偏见驱动模型学习错误的特征，因此训练这些模型的可用数据集通常包含未正确标注的数据。本文提出了一种面部情感识别系统，分别解决了自动面部检测和面部表情识别问题，后者是通过仅四个深度卷积神经网络的集合方法来完成的，而标签平滑技术则用于处理未贴标签的训练数据。拟议的系统使用专用图形处理单元（GPU）和141仅需13.48毫秒。