Occluded Facial Expression Recognition Enhanced through Privileged Information

In this paper, we propose a novel approach of occluded facial expression recognition under the help of non-occluded facial images. The non-occluded facial images are used as privileged information, which is only required during training, but not required during testing. Specifically, two deep neural networks are first trained from occluded and non-occluded facial images respectively. Then the non-occluded network is fixed and is used to guide the fine-tuning of the occluded network from both label space and feature space. Similarity constraint and loss inequality regularization are imposed to the label space to make the output of occluded network converge to that of the non-occluded network. Adversarial leaning is adopted to force the distribution of the learned features from occluded facial images to be close to that from non-occluded facial images. Furthermore, a decoder network is employed to reconstruct the non-occluded facial images from occluded features. Under the guidance of non-occluded facial images, the occluded network is expected to learn better features and classifier during training. Experiments on the benchmark databases with both synthesized and realistic occluded facial images demonstrate the superiority of the proposed method to state-of-the-art.