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应用于情报收集、执法和消费者应用。最近的研究表明，基于人脸的生物识别技术并不能在不同的人口结构中公平地发挥作用。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，以增强安全和公共安全。眼睛生物识别由眼睛内部和周围的区域组成，由于其准确性和保密性，为人脸生物识别提供了一种替代解决方案。此外，即使在遮盖面部的情况下，也可以使用常规相机获取眼部生物特征。该项目调查眼睛生物识别技术的公平性，并开发解决方案，以缓解人口统计差异之间的不同精度差距。这个项目跨越了一个高度多学科的研究领域，融合了工程学、统计学、数学、计算机和政策。这个项目的发现被用于更新工程课程，包括计算机视觉、图像分析、机器学习、深度学习和生物识别。该项目所取得的进展通过出版物、调查员网站和研讨会进行传播。这个项目还提供了扩大女性、少数族裔和本科生参与计算的机会。这个项目调查了在可见光和近红外(近红外)光谱中扫描的眼睛生物识别在人口统计学变化中的公平性。对为基于眼睛的个体分析而训练的机器和深度学习模型进行评估，以确定不同人口统计变量之间的不同准确率。使用可解释人工智能分析了机器和深度学习算法准确率不相等的原因。通过修改目标函数，使用集成技术，学习公平特征表示，开发了公平感知分类器。利用现有的和公开可用的眼部数据集对本研究中使用的分类器进行评估和安全性分析。建议的解决方案的有效性通过标准的生物识别性能和公平评估指标进行评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.