Small data assisting face image illumination normalization. (August 2021)

• Record Type: Journal Article
• Authors: Han, Xianjun; Wang, Huabin; Yang, Hongyu; Li, Xuejun
• Is Part Of: Computers & graphics. Volume 98(2021)
• Page Start: 82
• Page End: 92
• Publication Date: 2021-08
• Subjects: Small data assisting -- Illumination normalization -- Deep learning -- Face recognition; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2021.04.025
• Languages: English
• ISSNs: 0097-8493

Abstract

Highlights: A network generation model of small data assisting reconstruction is proposed. Small data learning as prior knowledge can guide the generation of large-scale features, and the reconstruction network further reconstructs the details of the original image. A new illumination normalization model is proposed. The assisting network is based on small data learning, which reduces the dependence of data size. The reconstruction network explores the different network parameters for different images, which avoids the poor generalization ability. Graphical abstract: Abstract: Image transfer based on deep learning methods can achieve good results in face illumination processing. However, data constraints and generalization ability restrict the further development of these methods in this field. In this paper, we propose small data assisting face image illumination normalization. For data constraints, we train the network model on a small number of image pairs. In terms of generalization ability, the proposed normalization network parameters are different for processing different face images. Small data learning can provide prior knowledge, and the reconstruction process can guide detail generation. Therefore, the small data learning network and the reconstruction network are complementary to each other in image generating mode when only a small quantity of data is available. We use this network mode to normalize the illumination and reconstruct the super-resolution of face images. After illumination normalization, super-resolution reconstruction can obtain more precise face information and further improve the face recognition rate. Experiments show that the proposed method has good normalization performance when only 500 face image pairs are used for training.