Using Deep Autoencoders for Facial Expression Recognition
This addresses the challenge of manually choosing high-dimensional features in facial expression recognition systems, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of feature selection and dimension reduction for facial expression recognition by using deep autoencoders, and the result showed that features extracted from stacked autoencoders outperformed other state-of-the-art techniques.
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.