Low-resolution face recognition based on feature-mapping face hallucination. (July 2022)

• Record Type: Journal Article
• Authors: Li, Sisi; Liu, Zhonghua; Wu, Di; Huo, Hua; Wang, Haijun; Zhang, Kaibing
• Is Part Of: Computers & electrical engineering. Volume 101(2022)
• Publication Date: 2022-07
• Subjects: Low-resolution face recognition -- Face hallucination -- Feature-mapping -- Identity-aware loss; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108136
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The image recognition approaches based on Convolutional Neural Network (CNN) have already achieved tremendous performance on super-resolution face images. However, there exist several challenges in face recognition field. For example, in very low-resolution face recognition (LRFR) environments, the accuracy will drop drastically. To address the issue, this paper proposes a novel face hallucination and recognition model for low resolution face images ground on feature-mapping. In the proposed model, a new loss function named identity-aware loss is also proposed. The proposed loss function is combined with the feature loss and image-content loss to jointly train models. The proposed model is evaluated on Labeled Faces in the Wild (LFW) dataset, which compared to progressive competing models. A large number of experimental results indicate that this model observably enhances the performance of recognition particularly when face images are very low-resolution. In addition, our model can perform high resolution face image reconstruction which is comparable to advanced approaches based on super-resolution in the field of visual quality, and achieve identity preservation of corresponding low resolution probe image.