DisVAE: Disentangled Variational Autoencoder for High-Quality Facial Expression Features

Abstract

Facial expression feature extraction suffers from high inter-subject variations caused by identity-related personal attributes. The extracted expression features are consistently entangled with other identity-related features, which has an influence on related facial expression tasks such as recognition and editing. To achieve high-quality expression features, a Disentangled Variational Autoencoder (DisVAE) is proposed to disentangle expression and identity features. The identity features are removed from the facial features via facial image reconstruction firstly, and then the remaining features represent expression components. Extensive experiments on three public datasets have shown that the proposed DisVAE can effectively disentangle expression and identity features, and extract expression features without the interfere of identity attributes. The high-quality expression features improve the performance of facial expression recognition and can be well applied to facial expression editing.