| Autor: |
Jameel, Samer Kais, Majidpour, Jafar, Al-Talabani, Abdulbasit K, Qadir, Jihad Anwar |
| Předmět: |
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| Zdroj: |
Computer Journal; Jul2023, Vol. 66 Issue 7, p1687-1697, 11p |
| Abstrakt: |
Most of research studies that have dealt with face corrupted images to the level of being unrecognizable by a human are based on full image reconstruction. In some real scenarios, a single corrupted image might need to be recognized among a limited number of available clean images. This study deals with face identification from artificially corrupted images with various kinds of noises. The work proposes a face identification conditional generative adversarial network (FI-CGAN) model to identify faces based on the CGAN. The proposed models reconstruct the corrupted image based on available clean images to map the corrupted image to a specific label. The classification is made using the nearest neighbor method with peak signal-to-noise ratio, mean squared error and structural similarity index as metrics. The study used the Specs on Faces dataset and achieved a satisfactory performance for face identification. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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