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Deep learning-based facial recognition systems have experienced increasedmedia attention due to exhibiting unfair behavior. Large enterprises, such asIBM, shut down their facial recognition and age prediction systems as aconsequence. Age prediction is an especially difficult application with theissue of fairness remaining an open research problem (e.g., predicting age fordifferent ethnicity equally accurate). One of the main causes of unfairbehavior in age prediction methods lies in the distribution and diversity ofthe training data. In this work, we present two novel approaches for datasetcuration and data augmentation in order to increase fairness through balancedfeature curation and increase diversity through distribution awareaugmentation. To achieve this, we introduce out-of-distribution detection tothe facial recognition domain which is used to select the data most relevant tothe deep neural network's (DNN) task when balancing the data among age,ethnicity, and gender. Our approach shows promising results. Our best-trainedDNN model outperformed all academic and industrial baselines in terms offairness by up to 4.92 times and also enhanced the DNN's ability to generalizeoutperforming Amazon AWS and Microsoft Azure public cloud systems by 31.88% and10.95%, respectively.

Fair and accurate age prediction using distribution aware data curation and augmentation