Face Verification Subject to Varying (Age, Ethnicity, and Gender)Demographics Using Deep Learning
Human facial appearance is strongly influenced by demographical characteristics such as categorical age, ethnicity, and gender with each category further partitioned into classes Black, White, Male, Female, Young (18 30) Middle Age (30 50) and Old (50 70) and groups mix of classes. Most subjects share a more similar appearance with their own demographic class than with other classes. We evaluate here the accuracy of automatic facial verification for subjects belonging to varying age, ethnicity, and gender categories. Towards that end, we use a convolutional neural network for feature extraction and show that our method yields better performance on individual demographics compared to a commercial face recognition engine. For one class demographic groups, we corroborate empirical findings that biometric performance on verification is relatively lower for females, young subjects in the 18 30 age group, and blacks. We then expand the scope of our method and evaluate the accuracy of face verification for several multiclass demographic groups. We discuss the results and make suggestions for improving face verification across varying demographics.