迄今为止，大多数考察人脸识别准确率中人口统计学差异的研究都分析了一对一匹配准确率，所使用的图像可被描述为“政府身份证件质量”。本文分析了不同人口群体之间的一对多人脸识别准确率，并考虑了探测图像中存在模糊和分辨率降低的情况，这些情况可能出现在“监控摄像头质量”的图像中。累积匹配特征曲线（CMC）不适合比较不同人口群体之间出现首位识别错误的倾向，因此我们在分析中使用三个指标：（1）配对与非配对分数分布之间众所周知的 d' 指标；（2）本研究提出的指标，即非配对分布高相似性尾部中的阈值与配对分布低相似性尾部中的阈值之间的绝对分数差；（3）在整组探测图像上，（配对首位分数 - 非配对首位分数）的分布。我们发现，一对多准确率中的人口统计学差异并不完全遵循在一对一匹配准确率中观察到的结果。此外，与一对一准确率不同，一对多准确率的人口群体比较可能受到不同人口群体中身份数量和图像数量差异的影响。更重要的是，我们表明，探测图像中模糊程度增加，或探测图像中人脸分辨率降低，会显著提高错误阳性识别率。而且我们表明，在这些高模糊或低分辨率条件下，男性/女性之间的人口统计学差异远大于非裔美国人/高加索人之间的差异。在针对“政府身份证件质量”图库处理“监控摄像头质量”探测图像的情境下，一对多准确率有可能崩溃，这一点非常重要。
英文摘要：
Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "government ID quality". This paper analyzes the accuracy of 1-to-many facial identification across demographic groups, and in the presence of blur and reduced resolution in the probe image as might occur in "surveillance camera quality" images. Cumulative match characteristic curves (CMC) are not appropriate for comparing propensity for rank-one recognition errors across demographics, and so we use three metrics for our analysis: (1) the well-known d' metric between mated and non-mated score distributions, and introduced in this work, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation in 1-to-many accuracy does not entirely follow what has been observed in 1-to-1 matching accuracy. Also, different from 1-to-1 accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. More importantly, we show that increased blur in the probe image, or reduced resolution of the face in the probe image, can significantly increase the false positive identification rate. And we show that the demographic variation in these high blur or low resolution conditions is much larger for male / female than for African-American / Caucasian. The point that 1-to-many accuracy can potentially collapse in the context of processing "surveillance camera quality" probe images against a "government ID quality" gallery is an important one.
Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；National University of Defense Technology（国防科技大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（瑞金医院，上海交通大学医学院）；Renji Hospital, Shanghai Jiao Tong University School of Medicine（仁济医院，上海交通大学医学院）
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models
Great Bay University（大湾大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shenzhen University（深圳大学）；Pengcheng Laboratory（鹏城实验室）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid（智能人工智能部门，马德里理工大学）；Departamento de Informática y Estadística, Universidad Rey Juan Carlos（信息与统计学部门，皇家胡安·卡洛斯大学）；ELLIS Unit Madrid（马德里ELLIS单位）
FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis
University of Pittsburgh, Swanson School of Engineering（匹兹堡大学斯旺森工程学院）；The University of North Carolina at Chapel Hill, Department of Computer Science（北卡罗来纳大学教堂山分校计算机科学系）；Arizona State University, School of Computing and Augmented Intelligence（亚利桑那州立大学计算与增强智能学院）；University of Pittsburgh, Department of Psychiatry（匹兹堡大学精神病学系）
Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis
Department of Computer Science, University of Rochester, NY, USA（罗切斯特大学计算机科学系）；Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA（印第安纳大学医学院生物统计学与健康数据科学系）；Department of Computer Science, University of Central Florida, FL, USA（佛罗里达州立大学计算机科学系）
Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photos
School of Intelligence Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）；School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
