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.

翻译：面部胡须的生长会显著改变人脸外观。图像对中的面部胡须风格会导致冒认者分布和真实分布的明显变化。此外，不同人口统计群体间面部胡须风格的分布差异可能导致跨群体相对准确率的错误认知。我们首先证明，尽管更大的训练集能提升对所有面部胡须风格的识别准确率，但由面部胡须风格引起的准确率差异始终存在，与训练集规模无关。随后，我们分析了训练数据中不同面部胡须风格比例的影响。我们利用Webface42M中同时包含剃须和蓄须图像的特定身份集合创建了平衡训练集。研究发现，即使人脸识别模型使用平衡的剃须/蓄须训练集进行训练，测试数据上的准确率差异仍未减小。接着，我们采用数据增强技术，借助面部关键点与胡须分割模型对面部胡须像素进行处理，进一步探究训练数据中胡须分布的影响。实验结果表明，面部胡须会导致剃须图像与蓄须图像间的准确率差距，且这种影响在非裔美国人与白种人之间存在显著差异。