🎓 📜 This repository holds my final year and dissertation project during my time at the University of Lincoln titled 'Deep Learning for Emotion Recognition in Cartoons'.
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README.md

dissertation

This repository holds my final year project during my time at the University of Lincoln titled 'Deep Learning for Emotion Recognition in Cartoons'.

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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.

Dataset

The dataset used in this dissertation is a collection of 4,800 Tom & Jerry face images.

Tom & Jerry Image Dataset (15MB)

Requirements

Install

git clone https://github.com/hako/dissertation
cd dissertation
sudo pip install -r requirements.txt

Usage

Download the above dataset, the folder must be named datasets. Below you can get started with the tools below.

Training / Classification / Visualisation

If you just want to train/classify or visualise the output of the network, use this tool:

training: (and show summary or results)
usage: train.py -t [-v|-s]

classification:
usage: train.py -c image.jpg

visualisation:
usage: train.py -V
Segmentation

Included in this repo are two Haar cascade files trained to detect Tom & Jerry faces. Note that if you choose this tool, you have to obtain the Tom & Jerry videos yourself.

If you want to segment the Tom & Jerry videos into images, use this tool:

python segmentation.py
Notebook

If you're the type that likes interactivity and experimentation, both the segmentation.py and the train.py files have their own Jupyter Notebooks in the notebooks/ folder. If you're using this, make sure the video and image datasets are in the folder.

Special Thanks

I would like to thank the following:

  • Professor Stefanos Kollias
  • My family and friends
  • The University of Lincoln Library

Citation

Hill, J.W., (2017). Deep Learning for Emotion Recognition in Cartoons

bibtex
@mastersthesis{hill17,
  author            = {John Wesley Hill},
  title             = {Deep Learning for Emotion Recognition in Cartoons},
  school            = {University of Lincoln},
  year              = {2017},
  document_type     = {Bachelor's Thesis},
  type              = {Bachelor Thesis},
}

Notes

Experiment Environment:

OS: Ubuntu GNU/Linux 16.04 LTS (x64)

GPU: Nvidia GeForce GTX 970

Tom & Jerry © Warner Bros. Entertainment, Inc

License

BSD