An Efficacious Method for Facial Expression Recognition: GAN Erased Facial Feature Network (GE2FN)

Abstract

We put forward a powerful facial expression recognition method based on removing the noise features from the input image to achieve a significant improvement in accuracy. To achieve this goal, we first exploit GAN network to generate a neutral face from the emotional face, and then apply two different convolution layers to extract emotional face features and neutral face features separately. Finally, we eliminate neutral face features from emotional face features to get pure “emotion features”, which are then used to get prediction results. The overall prediction network, termed GAN Erased Facial Feature Network (GE2FN) achieves 98.02% ACC on the CK+ dataset with 48x48 input. The accuracy rate is significantly improved compared to other approaches, including the current mainstream VGG approach