Multi-resolution dictionary learning for face recognition

In recent years, there has been a growing interest in the study of dictionary learning for face recognition. Most of the conventional dictionary learning methods focus only on a single resolution, which ignores the variability of resolutions of real-world face images. In order to address the above issue, this paper proposes a novel multi-resolution dictionary learning method that provides multiple dictionaries each being associated with a resolution. Especially, to enhance the robustness of the model, our method adds a relatively strong constraint to keep the similarity of representations obtained using different dictionaries in the training phase. We compare the proposed method to several state-of-the-art dictionary learning methods by applying this method to multi-resolution face recognition. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning methods. The MATLAB codes of the proposed method will be available at http://www.yongxu.org/lunwen.html. (C) 2019 Elsevier Ltd. All rights reserved.