Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units

TL;DR

This paper introduces two deep learning approaches, autoencoders and CNNs, for facial emotion recognition, demonstrating that a well-trained CNN can outperform existing methods on standard datasets.

Contribution

The paper presents a novel combination of autoencoder-based representations and a deep CNN for improved facial emotion detection.

Findings

CNN outperforms state-of-the-art methods after fine-tuning

Autoencoders create unique emotion representations

Models tested on posed and candid images

Abstract

Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The first method uses autoencoders to construct a unique representation of each emotion, while the second method is an 8-layer convolutional neural network (CNN). These methods were trained on the posed-emotion dataset (JAFFE), and to test their robustness, both the models were also tested on 100 random images from the Labeled Faces in the Wild (LFW) dataset, which consists of images that are candid than posed. The results show that with more fine-tuning and depth, our CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational autoencoders to improve their performance.