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Dissertation

Abstract

Emotion Recognition is a field that computers are getting very good at identifying; whether it’s through images, video or audio. Emotion Recognition has shown promising improvements when combined with classifiers and Deep Neural Networks showing a validation rate as high as 59% and a recognition rate of 56%.

The focus of this dissertation will be on facial based emotion recognition. This consists of detecting facial expressions in images and videos. While the majority of research uses human faces in an attempt to recognise basic emotions, there has been little research on whether the same deep learning techniques can be applied to faces in cartoons.

The system implemented in this paper, aims to classify at most three emotions (happiness, anger and surprise) of the 6 basic emotions proposed by psychologists Ekman and Friesen, with an accuracy of 80% for the 3 emotions. Showing promise of applications of deep learning and cartoons. This project is an attempt to examine if emotions in cartoons can be detected in the same way that human faces can.

As a first post, here is my dissertation I did at Lincoln, ‘Deep Learning for Emotion Recognition in Cartoons’. Most of the information is all on the README page on GitHub.

I really enjoyed doing this project and I hope you enjoy reading it too.

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© 2018 Wesley Hill

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