Balanced Faces in the Wild

This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples). Thus, along with the name (i.e., identification) labels and task protocols (e.g., list of pairs for face verification, pre-packaged data-table with additional metadata and labels, etc.), BFW groups into ethnicities (i.e., Asian (A), Black (B), Indian (I), and White (W)) and genders (i.e., Females (F) and Males (M)). Thus, the motivation and intent are that BFW will provide a proxy to characterize FR systems with demographic-specific analysis now possible. For instance, various confusion metrics and the predefined criteria (i.e., score threshold) are fundamental when characterizing performance ratings of FR systems. The following visualization summarizes the confusion metrics in a way that relates to the different measurements.

本研究旨在探讨自动人脸识别（FR）中的偏差问题。具体而言，受试者根据性别、种族和年龄被划分为预定义的子群体。本研究提出了一种名为Balanced Faces in the Wild（BFW）的新型图像集，该图像集在八个子群体中实现平衡（即，100位受试者的800张人脸图像，每位受试者提供25张人脸样本）。因此，除了名称（即，识别标签）和任务协议（例如，人脸验证的配对列表、包含额外元数据和标签的预包装数据表等）之外，BFW还将受试者按种族（即，亚洲（A）、黑人（B）、印度人（I）和白人（W））和性别（即，女性（F）和男性（M））进行分组。因此，BFW的动机和意图在于，它将为具有人口统计学特定分析的FR系统提供一种表征的替代方案。例如，在表征FR系统的性能评级时，各种混淆度指标和预定义的标准（即，分数阈值）是基本要素。以下可视化以与不同测量相关的方式总结了混淆度指标。