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

Leveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces