Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces
This work addresses emotion recognition for human-computer interaction, but it is incremental as it builds upon an existing method (EmotiRAM).
The paper tackled the problem of improving emotion recognition from facial expressions by integrating Facial Action Units (AUs) recognition techniques, specifically enhancing the facial encoding module of the EmotiRAM approach to achieve better performance.
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.