  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.
