平衡偏见和保护隐私 在野生中平衡的面孔上
概括
面部识别 (FR) 模型显示人口偏差. 一种新的域调整方法提高了子组的性能,并删除了人口统计数据,提高了面部识别系统中的公平性和隐私.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 面部识别 (FR) 模型表现出显著的人口偏差,特别是在种族和性别子组之间.
- 当前的FR性能指标往往无法捕捉特定子组的准确性,导致不同人群的错误率不可靠.
- 真正/假冒对分类的单一得分值是不理想的,并加剧了绩效差异.
研究的目的:
- 引入"平衡野生面孔" (BFW) 数据集,用于测量人口亚组中的FR偏差.
- 提出和评估一个新的领域适应学习方案,以减轻绩效失衡,并从面部特征中删除人口统计信息.
- 证明拟议方法在防止分组分类器学习人口信息方面的有效性.
主要方法:
- 开发和利用"野生平衡面孔" (BFW) 数据集用于偏见评估.
- 使用最先进的神经网络提取的面部特征实施域适应学习方案.
- 评估该方法在删除人口统计相关性时保存身份信息的能力.
主要成果:
- 根据BFW数据集,可以详细描述每个人口亚组的FR表现.
- 拟议的域调整方案显著提高了平均FR绩效,并减少了子组差异.
- 域调整方案处理的面部特征阻止子组分类器学习人口特征,确保隐私和公平.
结论:
- 解决FR人口偏差对于公平可靠的系统至关重要.
- 新型域名适应方法有效地减轻了偏见,并通过删除人口信息来保护隐私.
- BFW数据集和拟议的方法为推进公平和强大的面部识别技术提供了有价值的工具.
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