Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics

Understand

The most popular face recognition benchmarks assume a distribution of subjects without much attention to their demographic attributes.

• In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recognition.
• The main aim of this study is focused on a better understanding of the feature space generated by deep models, and the performance achieved over different demographic groups.
• We also propose a general formulation of algorithmic discrimination with application to face biometrics.