Abstract
Recognition of facial expressions plays an important role in understanding human behavior, classroom assessment, customer feedback, education, business, and many other human-machine interaction applications. Some researchers have realized that using features corresponding to different scales can improve the recognition accuracy, but there is a lack of a systematic study to utilize the scale information. In this work, we proposed a hierarchical scale convolutional neural network (HSNet) for facial expression recognition, which can systematically enhance the information extracted from the kernel, network, and knowledge scale. First, inspired by that the facial expression can be defined by different size facial action units and the power of sparsity, we proposed dilation Inception blocks to enhance kernel scale information extraction. Second, to supervise relatively shallow layers for learning more discriminated features from different size feature maps, we proposed a feature guided auxiliary learning approach to utilize high-level semantic features to guide the shallow layers learning. Last, since human cognitive ability can progressively be improved by learned knowledge, we mimicked such ability by knowledge transfer learning from related tasks. Extensive experiments on lab-controlled, synthesized, and in-the-wild databases showed that the proposed method substantially boosts performance, and achieved state-of-the-art accuracy on most databases. Ablation studies proved the effectiveness of modules in the proposed method.