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Fairness in Biometrics: a figure of merit to assess biometric verification systems

Tiago De Freitas Pereira, Sébastien Marcel

Machine learning-based (ML) systems are being largely deployed since the last decade in a myriad of scenarios impacting several instances in our daily lives. With this vast sort of applications, aspects of fairness start to rise in the spotlight due to the social impact that this can get in minorities. In this work aspects of fairness in biometrics are addressed. First, we introduce the first figure of merit that is able to evaluate and compare fairness aspects between multiple biometric verification systems, the so-called Fairness Discrepancy Rate (FDR). A use case with two synthetic biometric systems is introduced and demonstrates the potential of this figure of merit in extreme cases of fair and unfair behavior. Second, a use case using face biometrics is presented where several systems are evaluated compared with this new figure of merit using three public datasets exploring gender and race demographics.

2020

A Comprehensive Experimental and Reproducible Study on Selfie Biometrics in Multistream and Heterogeneous Settings

Tiago De Freitas Pereira, Guillaume Heusch, Sébastien Marcel

This contribution presents a new database to address current challenges in face recognition. It contains face video sequences of 75 individuals acquired either through a laptop webcam or when mimicking the front-facing camera of a smartphone. Sequences have been acquired with a device allowing to record visual, near-infrared and depth data at the same time. Recordings have been made across three sessions with different, challenging illumination conditions and variations in pose. Together with the database, several experimental protocols are provided and correspond to real world scenarios, when a mismatch in conditions between enrollment and probe images occurs. A comprehensive set of baseline experiments using publicly available baseline algorithms show that extreme illumination conditions and pose variations are remaining issues. However, the usage of different data domains -and their fusion -allows to mitigate such variation. Finally, experiments on heterogeneous face recognition are also presented using a state-of-the-art model based on deep neural networks, and showed better performance. When applied to other tasks, this model proved to surpass all existing baselines as well. The data, as well as the code to reproduce all experiments are made publicly available to help foster research in selfie biometrics using latest imaging devices.

2019