Dictionary Representation of Deep Features for Occlusion-Robust Face Recognition

Deep learning has achieved exciting results in face recognition; however, the accuracy is still unsatisfying for occluded faces. To improve the robustness for occluded faces, this paper proposes a novel deep dictionary representation-based classification scheme, where a convolutional neural network is employed as the feature extractor and followed by a dictionary to linearly code the extracted deep features. The dictionary is composed by a gallery part consisting of the deep features of the training samples and an auxiliary part consisting of the mapping vectors acquired from the subjects either inside or outside the training set and associated with the occlusion patterns of the testing face samples. A squared Euclidean norm is used to regularize the coding coefficients. The proposed scheme is computationally efficient and is robust to large contiguous occlusion. In addition, the proposed scheme is generic for both the occluded and non-occluded face images and works with a single training sample per subject. The extensive experimental evaluations demonstrate the superior performance of the proposed approach over other state-of-the-art algorithms.