Using Deep Autoencoders for Facial Expression Recognition

TL;DR

This paper explores the use of deep autoencoders for automatic feature selection and dimension reduction in facial expression recognition, demonstrating superior performance over existing methods.

Contribution

It introduces a novel application of deep autoencoders for feature extraction in facial expression recognition, showing improved results over traditional techniques.

Findings

Autoencoders outperform other feature selection methods

Deep autoencoders improve recognition accuracy

Multiple hidden layer configurations tested

Abstract

Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature. Selecting the most important features is a very crucial task for systems like facial expression recognition. This paper investigates the performance of deep autoencoders for feature selection and dimension reduction for facial expression recognition on multiple levels of hidden layers. The features extracted from the stacked autoencoder outperformed when compared to other state-of-the-art feature selection and dimension reduction techniques.