  The representation used for Facial Expression Recognition (FER) usually
contain expression information along with other variations such as identity and
illumination. In this paper, we propose a novel Disentangled Expression
learning-Generative Adversarial Network (DE-GAN) to explicitly disentangle
facial expression representation from identity information. In this learning by
reconstruction method, facial expression representation is learned by
reconstructing an expression image employing an encoder-decoder based
generator. This expression representation is disentangled from identity
component by explicitly providing the identity code to the decoder part of
DE-GAN. The process of expression image reconstruction and disentangled
expression representation learning is improved by performing expression and
identity classification in the discriminator of DE-GAN. The disentangled facial
expression representation is then used for facial expression recognition
employing simple classifiers like SVM or MLP. The experiments are performed on
publicly available and widely used face expression databases (CK+, MMI,
Oulu-CASIA). The experimental results show that the proposed technique produces
comparable results with state-of-the-art methods.
