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Face attribute research has so far used only simple binary attributes forfacial hair; e.g., beard / no beard. We have created a new, more descriptivefacial hair annotation scheme and applied it to create a new facial hairattribute dataset, FH37K. Face attribute research also so far has not dealtwith logical consistency and completeness. For example, in prior research, animage might be classified as both having no beard and also having a goatee (atype of beard). We show that the test accuracy of previous classificationmethods on facial hair attribute classification drops significantly if logicalconsistency of classifications is enforced. We propose a logically consistentprediction loss, LCPLoss, to aid learning of logical consistency acrossattributes, and also a label compensation training strategy to eliminate theproblem of no positive prediction across a set of related attributes. Using anattribute classifier trained on FH37K, we investigate how facial hair affectsface recognition accuracy, including variation across demographics. Resultsshow that similarity and difference in facial hairstyle have important effectson the impostor and genuine score distributions in face recognition. The codeis at https:// github.com/ HaiyuWu/ LogicalConsistency.

Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning