Leveraging Previous Facial Action Units Knowledge for Emotion
  Recognition on Faces
Pietro B. S. Masur Willams Costa Lucas S. Figueredo Veronica Teichrieb
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.