论文标题
面部识别性别歧视吗？不，性别发型和生物学是
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are
论文作者
论文摘要
最近的新闻文章指责面对面的认可是“有偏见”，“性别歧视”或“种族主义者”。在研究文献中达成共识，女性的面部识别精度较低，女性通常既具有较高的错误匹配率，又具有较高的错误匹配率。但是，几乎没有发表的研究旨在确定女性准确性降低的原因。例如，2019年的面部识别供应商测试记录了各种算法和数据集中女性准确性较低的精度，还列出了标题“我们没有做的事情”下的“分析因果”。我们提出了第一个实验分析，以确定在先前研究观察到的数据集中女性较低面部识别准确性的主要原因。在测试图像中控制相等数量的可见脸可逆转女性的明显较高的虚假不匹配率。同样，主成分分析表明，两个不同女性的图像本质上比两种不同的男性更相似，这可能会考虑到错误匹配率的差异。
Recent news articles have accused face recognition of being "biased", "sexist" or "racist". There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate. However, there is little published research aimed at identifying the cause of lower accuracy for females. For instance, the 2019 Face Recognition Vendor Test that documents lower female accuracy across a broad range of algorithms and datasets also lists "Analyze cause and effect" under the heading "What we did not do". We present the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result. Controlling for equal amount of visible face in the test images reverses the apparent higher false non-match rate for females. Also, principal component analysis indicates that images of two different females are inherently more similar than of two different males, potentially accounting for a difference in false match rates.
