Facial Expression Recognition with Global Multiscale and Local Attention Network

Due to problems such as occlusion and pose variation, facial expression recognition (FER) in the wild is a challenging classification task. This paper proposes a global multiscale and local attention network (GL-VGG) based on the VGG structure, which consists of four modules: a VGG base module, a dropblock module, a global multiscale module, and a local attention module. The base module pre-extracts features, the dropblock module prevents overfitting in the convolutional layers, the global multiscale module is used to learn different receptive field features in the global perception domain, which reduces the susceptibility of deeper convolution towards occlusion and variant pose, and the local attention module guides the network to focus on local rich features, which releases the interference of occlusion on FER in the wild. Experiments on two public wild FER datasets show that our GL-VGG approach outperforms the baseline and other state-of-the-art methods with 88.33% on RAF-DB and 74.17% on FER2013.