Facial Expression Representation Learning by Synthesizing Expression Images
This addresses the challenge of separating expression and identity information in facial recognition, which is incremental as it builds on existing disentanglement and residual learning approaches.
The paper tackled the problem of facial expression recognition by disentangling expression from identity features using a novel DE-GAN method, achieving comparable or better results than state-of-the-art methods on datasets like CK+, MMI, and Oulu-CASIA.
Representations used for Facial Expression Recognition (FER) usually contain expression information along with identity features. In this paper, we propose a novel Disentangled Expression learning-Generative Adversarial Network (DE-GAN) which combines the concept of disentangled representation learning with residue learning to explicitly disentangle facial expression representation from identity information. In this method the facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. Unlike previous works using only expression residual learning for facial expression recognition, our method learns the disentangled expression representation along with the expressive component recorded by the encoder of DE-GAN. In order to improve the quality of synthesized expression images and the effectiveness of the learned disentangled expression representation, expression and identity classification is performed by the discriminator of DE-GAN. Experiments performed on widely used datasets (CK+, MMI, Oulu-CASIA) show that the proposed technique produces comparable or better results than state-of-the-art methods.