  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.
