Abstract | ||
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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. |
Year | DOI | Venue |
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2022 | 10.1016/j.compeleceng.2022.108136 | Computers and Electrical Engineering |
Keywords | DocType | Volume |
Low-resolution face recognition,Face hallucination,Feature-mapping,Identity-aware loss | Journal | 101 |
ISSN | Citations | PageRank |
0045-7906 | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Sisi Li | 1 | 0 | 0.34 |
Zhonghua Liu | 2 | 115 | 11.12 |
Di Wu | 3 | 0 | 0.34 |
Hua Huo | 4 | 0 | 0.68 |
Haijun Wang | 5 | 0 | 0.68 |
Kaibing Zhang | 6 | 568 | 23.60 |