论文标题
探测跨性别的移动眼生物识别方法在VISOB 2.0数据集上
Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset
论文作者
论文摘要
最近的研究质疑了对黑皮肤的人和女性的基于面部的识别和属性分类方法（例如性别和种族）的公平性。可见光谱中的眼部生物识别技术是面部生物识别技术的替代解决方案，这要归功于其准确性，安全性，可抵抗面部表达以及在移动设备中的易用性。随着最近的Covid-19危机，在面罩的情况下，眼生物识别技术比面部生物识别具有进一步的优势。但是，直到现在尚未研究眼生物识别技术的公平性。这项第一项研究旨在探讨男性和女性基于眼的身份验证和性别分类方法的公平性。为此，Visob $ 2.0 $数据集及其性别注释及其性别注释用于基于Resnet-50，Mobilenet-V2和LightCNN-29模型的眼部生物识别方法的公平分析。实验结果表明，基于较低的虚假匹配率（FMR）和曲线下的整体面积（AUC），基于眼的移动用户对男性和女性的表现相同。例如，平均获得LightCNN-29的女性AUC为0.96，男性为0.95。但是，基于眼区域的基于深度学习的性别分类模型，男性在基于深度学习的性别分类模型中的表现显着优于女性。
Recent research has questioned the fairness of face-based recognition and attribute classification methods (such as gender and race) for dark-skinned people and women. Ocular biometrics in the visible spectrum is an alternate solution over face biometrics, thanks to its accuracy, security, robustness against facial expression, and ease of use in mobile devices. With the recent COVID-19 crisis, ocular biometrics has a further advantage over face biometrics in the presence of a mask. However, fairness of ocular biometrics has not been studied till now. This first study aims to explore the fairness of ocular-based authentication and gender classification methods across males and females. To this aim, VISOB $2.0$ dataset, along with its gender annotations, is used for the fairness analysis of ocular biometrics methods based on ResNet-50, MobileNet-V2 and lightCNN-29 models. Experimental results suggest the equivalent performance of males and females for ocular-based mobile user-authentication in terms of genuine match rate (GMR) at lower false match rates (FMRs) and an overall Area Under Curve (AUC). For instance, an AUC of 0.96 for females and 0.95 for males was obtained for lightCNN-29 on an average. However, males significantly outperformed females in deep learning based gender classification models based on ocular-region.
