3 research outputs found
Emotion Generation and Recognition: A StarGAN Approach
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
Image Generation and Recognition (Emotions)
Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et
al., and have since been extended into multiple computer vision applications.
This report provides a thorough survey of recent GAN research, outlining the
various architectures and applications, as well as methods for training GANs
and dealing with latent space. This is followed by a discussion of potential
areas for future GAN research, including: evaluating GANs, better understanding
GANs, and techniques for training GANs. The second part of this report outlines
the compilation of a dataset of images `in the wild' representing each of the 7
basic human emotions, and analyses experiments done when training a StarGAN on
this dataset combined with the FER2013 dataset
Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild
Emotions play an important role in people's life. Understanding and
recognising is not only important for interpersonal communication, but also has
promising applications in Human-Computer Interaction, automobile safety and
medical research. This project focuses on extending the emotion recognition
database, and training the CNN + RNN emotion recognition neural networks with
emotion category representation and valence \& arousal representation. The
combined models are constructed by training the two representations
simultaneously. The comparison and analysis between the three types of model
are discussed. The inner-relationship between two emotion representations and
the interpretability of the neural networks are investigated. The findings
suggest that categorical emotion recognition performance can benefit from
training with a combined model. And the mapping of emotion category and valence
\& arousal values can explain this phenomenon