FaiResGAN: Fair and robust blind face restoration with biometrics preservation

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
George Azzopardi , Antonio Greco , Mario Vento
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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.
fairresgan:具有生物特征保存的公平和健壮的盲人脸修复
现代计算机视觉技术使系统能够检测、识别和分析面部特征,但当图像有噪声、模糊或低质量时,就会出现挑战。盲人脸修复,旨在恢复高质量的面部图像,而不需要事先知道退化,解决了这个问题。在本文中,我们介绍了公平恢复GAN (fairresgan),这是一种新型的生成对抗网络(GAN),旨在平衡面部恢复与软生物特征(身份,种族,年龄和性别)的保存。我们的模型结合了一个伪随机批量组合算法来促进公平和减轻偏见,以及一个模拟监控图像中典型腐败的现实退化模型。实验结果表明,FaiResGAN在数量和质量上都优于目前最先进的盲人脸恢复方法。一项涉及40名参与者的用户研究表明,70%的用户更喜欢fairesgan修复的图像。此外,在VGGFace2、UTKFace和FairFace数据集上的测试表明,FaiResGAN在保留软生物特征属性和确保不同性别和种族的公平恢复方面具有卓越的性能。
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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
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.
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