A Deep Structure for Facial Expression Recognition under Partial Occlusion

According to the complex manifestation of human facial expression in realistic environment, occlusion problem has become a new challenge and a hot spot in the field of expression recognition. To make facial expression recognition applied in broader way, the main work is to increase the accuracy under different partial occlusion with feasible robust, which is limited by the information missing and insufficient training with fewer samples. Therefore, an algorithm with a deep structure has been proposed in this paper dealing with four types of frequently occurred occlusion. As a classic method, the Gabor filter is used for feature extraction at first. Then, multi-layers network is used to pre-train the training data samples, with re-describing the input Gabor features in complex way and fine-tuning the weights to refine the learning model. The experimental results on JAFFE database show that the proposed method is valid to achieve better recognition rate especially for partial occlusion on eyes and mouth.