SaTC: CORE: Small: Probing Fairness of Ocular Biometrics Methods Across Demographic Variations

与识别人的身份和特征相关的生物识别技术已被广泛用于情报收集、执法和消费者应用。最近的研究表明，基于面部的生物识别技术在人口统计学差异中并不公平。迫切需要研究公平的生物识别解决方案和模式，以实现准确，公平和值得信赖的技术，以增强安全性和公共安全。眼睛生物识别技术由眼睛内部和周围的区域组成，由于其准确性和隐私性，它为面部生物识别提供了另一种解决方案。此外，即使在存在面部覆盖的情况下，也可以使用常规相机来获取眼部生物特征。 该项目调查了眼部生物识别技术的公平性，并开发了解决方案，以减轻人口统计差异的不平等准确性差距。 该项目跨越了一个高度多学科的研究领域，它集成了工程，统计，数学，计算和政策。该项目的研究结果用于更新工程课程，包括计算机视觉，图像分析，机器学习，深度学习和生物识别。该项目取得的进展通过出版物、调查员网站和研讨会传播。该项目还提供了机会，以扩大妇女，代表性不足的少数民族，和本科生在computing.This项目的参与调查的公平性，在可见光和近红外（近红外）光谱扫描的眼部生物特征在人口统计学的变化。针对基于视觉的个体分析训练的机器和深度学习模型，评估了不同人口统计学差异的不平等准确率。机器和深度学习算法中准确率不相等的原因使用可解释的AI进行分析。公平意识的分类器开发修改的目标函数，使用集成技术，并学习公平的特征表示。现有的和公开可用的眼部数据集用于本研究中使用的分类器的评估和安全性分析。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估。

Biometrics technology related to recognizing the identities and traits of people has been widely adopted in intelligence gathering, law enforcement, and consumer applications. Recent studies suggest that face-based biometric technology does not work equitably across demographic variations. There is a pressing need to investigate fair biometric solutions and modalities toward accurate, fair, and trustworthy technology for enhanced security and public safety. Ocular biometrics, which consists of regions in and around the eyes, offers an alternate solution to face biometrics due to its accuracy and privacy. Furthermore, ocular biometrics can be acquired using regular cameras even in the presence of face covering. This project investigates the fairness of ocular biometric technology and develops solutions to mitigate unequal accuracy gaps across demographic variations. This project spans a highly multidisciplinary research area, which integrates engineering, statistics, mathematics, computing, and policy. The findings of this project are used to update the engineering curricula, including computer vision, image analysis, machine learning, deep learning, and biometrics. The advances made in this project are disseminated through publications, the investigator website, and seminars. This project also provides opportunities to broaden the participation of women, underrepresented minorities, and undergraduate students in computing.This project investigates the fairness of ocular biometrics scanned in visible and NIR (Near-infrared) spectrum across demographic variations. The machine and deep learning models trained for ocular-based individual analysis are evaluated for unequal accuracy rates across demographic variations. The cause of unequal accuracy rates in the machine and deep learning algorithms are analyzed using explainable AI. The fairness-aware classifiers are developed by modifying the objective function, using ensemble techniques, and learning fair feature representation. Existing and publicly available ocular datasets are utilized for the evaluation and security analysis of the classifiers used in this study. The efficacy of the proposed solutions is assessed through standard biometric performance and fairness evaluation metrics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.