Emotion Generation and Recognition: A StarGAN Approach
Computer Vision and Pattern Recognition
2019-10-25 v1 Machine Learning
Audio and Speech Processing
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1910.11090,
title = {Emotion Generation and Recognition: A StarGAN Approach},
author = {Aritra Banerjee and Dimitrios Kollias},
journal= {arXiv preprint arXiv:1910.11090},
year = {2019}
}