Intensity Enhancement via GAN for Multimodal Facial Expression Recognition

Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.