{"title":"FaiResGAN: Fair and robust blind face restoration with biometrics preservation","authors":"George Azzopardi , Antonio Greco , Mario Vento","doi":"10.1016/j.imavis.2025.105575","DOIUrl":null,"url":null,"abstract":"<div><div>Modern computer vision technologies enable systems to detect, recognize, and analyze facial features, but challenges arise when images are noisy, blurred, or low quality. Blind face restoration, which aims to recover high-quality facial images without prior knowledge of degradation, addresses this issue. In this paper, we introduce Fair Restoration GAN (FaiResGAN), a novel Generative Adversarial Network (GAN) designed to balance face restoration with the preservation of soft biometrics (identity, ethnicity, age, and gender). Our model incorporates a pseudo-random batch composition algorithm to promote fairness and mitigate bias, alongside a realistic degradation model simulating corruptions typical in surveillance images. Experimental results show that FaiResGAN outperforms state-of-the-art blind face restoration methods, both quantitatively and qualitatively. A user study involving 40 participants showed that FaiResGAN-restored images were preferred by 70% of users. Additionally, tests on VGGFace2, UTKFace, and FairFace datasets demonstrate FaiResGAN’s superior performance in preserving soft biometric attributes and ensuring fair restoration across different genders and ethnicities.</div></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":"160 ","pages":"Article 105575"},"PeriodicalIF":4.2000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885625001635","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Modern computer vision technologies enable systems to detect, recognize, and analyze facial features, but challenges arise when images are noisy, blurred, or low quality. Blind face restoration, which aims to recover high-quality facial images without prior knowledge of degradation, addresses this issue. In this paper, we introduce Fair Restoration GAN (FaiResGAN), a novel Generative Adversarial Network (GAN) designed to balance face restoration with the preservation of soft biometrics (identity, ethnicity, age, and gender). Our model incorporates a pseudo-random batch composition algorithm to promote fairness and mitigate bias, alongside a realistic degradation model simulating corruptions typical in surveillance images. Experimental results show that FaiResGAN outperforms state-of-the-art blind face restoration methods, both quantitatively and qualitatively. A user study involving 40 participants showed that FaiResGAN-restored images were preferred by 70% of users. Additionally, tests on VGGFace2, UTKFace, and FairFace datasets demonstrate FaiResGAN’s superior performance in preserving soft biometric attributes and ensuring fair restoration across different genders and ethnicities.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.