Facial Expression Recognition Method Based on Stacked Denoising Autoencoders and Feature Reduction

Abstract

Based on the deep learning theory, a novel facial expression recognition method, which utilizes both Principal Component Analysis (PCA) and stacked denoising autoencoders (SDAE), is proposed in this paper. At first, PCA is used as a linear dimensionality reduction method on the expression features, and subsequently non-linear dimensionality is further learnt by a greed layer-wise method of stacked denoising autoencoders. Then, some meaningful and low-dimensioned expression features can be learnt and used to classify. The comparative experiment results show that the proposed method is more effective than some other expression recognition methods based on deep learning theories and it can also get higher expression recognition accuracy than traditional non-deep learning based expression recognition methods.