Representations used for Facial Expression Recognition (FER) are usually contaminated with identity specific features. In this paper, we propose a novel Reenactment-based Expression-Representation Learning Generative Adversarial Network (REL-GAN) that employs the concept of face reenactment to disentangle facial expression features from identity information. In this method, the facial expression representation is learned by reconstructing an expression image employing an encoder-decoder based generator. More specifically, our method learns the disentangled expression representation by transferring the expression information from the source image to the identity of the target image. Experiments performed on widely used datasets (BU-3DFE, CK+, Oulu-CASIA, SEFW) show that the proposed technique produces comparable or better results than state-of-the-art methods.