Face attribute research has so far used only simple binary attributes for facial hair; e.g., beard / no beard. We have created a new, more descriptive facial hair annotation scheme and applied it to create a new facial hair attribute dataset, FH37K. Face attribute research also so far has not dealt with logical consistency and completeness. For example, in prior research, an image might be classified as both having no beard and also having a goatee (a type of beard). We show that the test accuracy of previous classification methods on facial hair attribute classification drops significantly if logical consistency of classifications is enforced. We propose a logically consistent prediction loss, LCPLoss, to aid learning of logical consistency across attributes, and also a label compensation training strategy to eliminate the problem of no positive prediction across a set of related attributes. Using an attribute classifier trained on FH37K, we investigate how facial hair affects face recognition accuracy, including variation across demographics. Results show that similarity and difference in facial hairstyle have important effects on the impostor and genuine score distributions in face recognition.

翻译：面部属性研究至今仅使用简单的二元属性描述面部毛发，例如有胡须/无胡须。我们创建了一种更具描述性的新面部毛发标注方案，并据此构建了新的面部毛发属性数据集FH37K。此外，现有面部属性研究尚未解决逻辑一致性与完整性问题。例如，在先前研究中，同一图像可能同时被分类为无胡须和留有山羊胡（一种胡须类型）。研究表明，若强制实施分类结果的逻辑一致性，现有分类方法在面部毛发属性分类中的测试准确率将显著下降。我们提出一种逻辑一致预测损失函数LCPLoss，用于促进跨属性逻辑一致性的学习，同时提出标签补偿训练策略，以消除相关属性集内无正向预测的问题。基于FH37K训练的属性分类器，我们探究了面部毛发对识别准确率的影响，包括跨人口统计特征的差异。结果表明，面部毛发风格的相似性与差异会显著影响人脸识别中的冒认者分数与真实者分数分布。