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Found 4 papers, 2 papers with code
Beard Segmentation and Recognition Bias
To investigate the effect of facial hair in a rigorous manner, we first created a set of fine-grained facial hair annotations to train a segmentation model and evaluate its accuracy across African-American and Caucasian face images.
Exploring Causes of Demographic Variations In Face Recognition Accuracy
In recent years, media reports have called out bias and racism in face recognition technology.
Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
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.
Consistency and Accuracy of CelebA Attribute Values
Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency.
