Appearance of a face can be greatly altered by growing a beard and mustache.
The facial hairstyles in a pair of images can cause marked changes to the
impostor distribution and the genuine distribution. Also, different
distributions of facial hairstyle across demographics could cause a false
impression of relative accuracy across demographics. We first show that, even
though larger training sets boost the recognition accuracy on all facial
hairstyles, accuracy variations caused by facial hairstyles persist regardless
of the size of the training set. Then, we analyze the impact of having
different fractions of the training data represent facial hairstyles. We
created balanced training sets using a set of identities available in
Webface42M that both have clean-shaven and facial hair images. We find that,
even when a face recognition model is trained with a balanced clean-shaven /
facial hair training set, accuracy variation on the test data does not
diminish. Next, data augmentation is employed to further investigate the effect
of facial hair distribution in training data by manipulating facial hair pixels
with the help of facial landmark points and a facial hair segmentation model.
Our results show facial hair causes an accuracy gap between clean-shaven and
facial hair images, and this impact can be significantly different between
African-Americans and Caucasians.

面部的着装出现变化可以大大改变外貌。虽然更大的训练集可以提高对所有面部着装的识别准确率，但是由于面部着装的影响，准确率的变化不受训练集大小的影响。此外，不同人群中面部着装的分布可能导致误导人们对不同人群之间识别准确率的印象。通过分析面部着装在训练数据中不同比例的影响，我们发现，即使使用平衡的无胡须和有胡须的训练集进行训练，测试数据上的准确率变化并没有减少。进一步研究面部着装分布对训练数据的影响，我们采用数据增强技术对面部着装像素进行操作，并通过面部标志点和面部分割模型的帮助。我们的结果表明，面部着装导致了无胡须和有胡须图像之间的准确率差距，并且这种影响在非洲裔美国人和白人之间差异显著。