People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are the biggest transmitters of non-verbal cues capable of being correlated with emotions. Several techniques are based on Convolutional Neural Networks (CNNs) to extract information in a machine learning process. However, simple CNNs are not always sufficient to locate points of interest on the face that can be correlated with emotions. In this work, we intend to expand the capacity of emotion recognition techniques by proposing the usage of Facial Action Units (AUs) recognition techniques to recognize emotions. This recognition will be based on the Facial Action Coding System (FACS) and computed by a machine learning system. In particular, our method expands over EmotiRAM, an approach for multi-cue emotion recognition, in which we improve over their facial encoding module.

翻译：人们天然能够理解情绪，因此让机器具备同样的能力将为人类-计算机交互开辟新路径。面部表情作为最具信息传递能力的非语言信号，可与情绪建立关联，因而对情绪识别技术具有重要价值。现有多种技术基于卷积神经网络（CNN）在机器学习过程中提取信息，但简单的CNN网络往往不足以定位面部与情绪相关的兴趣点。本研究旨在通过引入面部动作单元（AUs）识别技术来扩展情绪识别能力。该识别方法基于面部动作编码系统（FACS），并通过机器学习系统实现计算。具体而言，我们的方法是在多线索情绪识别框架EmotiRAM的基础上进行扩展，重点改进了其面部编码模块。