Emotion Generation and Recognition: A StarGAN Approach
This work addresses emotion recognition and generation for applications like human-computer interaction, but it is incremental as it applies an existing method to a new dataset.
The authors tackled emotion generation and recognition by training an existing StarGAN model on a new dataset of 4K videos annotated with valence-arousal scores for seven basic emotions, resulting in a system that generates emotions based on these scores and was tested manually for efficiency.
The main idea of this ISO is to use StarGAN (A type of GAN model) to perform training and testing on an emotion dataset resulting in a emotion recognition which can be generated by the valence arousal score of the 7 basic expressions. We have created an entirely new dataset consisting of 4K videos. This dataset consists of all the basic 7 types of emotions: Happy, Sad, Angry, Surprised, Fear, Disgust, Neutral. We have performed face detection and alignment followed by annotating basic valence arousal values to the frames/images in the dataset depending on the emotions manually. Then the existing StarGAN model is trained on our created dataset after which some manual subjects were chosen to test the efficiency of the trained StarGAN model.