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Reconstruction of Partially Occluded Facial Image for Classification

The recent literature indicates that face recognition and facial expression classification has achieved a high accuracy on benchmark datasets with a large number of face images in the wild. On the other hand, unlike the purpose of recognizing as many people as possible, real applications for families or companies usually aim to recognize a small group of people as accurate as possible. In case of the face is partially occluded, convolutional solutions always simply put images with occlusions into the training dataset and hope the convolution neural network learn a model robust to partial occlusion. These processes not only increase the burden of learning, but also affect the model to identify normal images without occlusions. In this paper, we investigate the research problem of facial image reconstruction to discard the influence of partial occlusion. Based on the phenomenon that human faces are roughly symmetrical, we propose to reconstruct the facial information of occluded areas with the intact half face. Specifically, occlusion on the left‐half face is reconstructed with a linear combination of features on the right‐half face and vice versa. The process is modeled by keeping row sparsity for the coefficient matrix with l2,1‐norm regularization while minimizing the reconstruction error. An alternative iterative algorithm is proposed to solve the optimization problem. To validate the effectiveness of the reconstruction, we fine‐tune a pretrained model, AlexNet, training on normal face images and test with various occluded images. Extensive experimental results show that our method has improved the classification performance effectively. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.