Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models

TL;DR

This paper investigates how image distortions affect bias in pre-trained face recognition models, revealing that distortions can increase performance disparities across gender and race groups.

Contribution

It provides the first systematic analysis of the impact of image distortions on bias in pre-trained face recognition models across subgroups.

Findings

Image distortions influence model performance gaps across subgroups.

Performance disparities vary with different types of distortions.

Pre-trained models may become more biased under certain distortions.

Abstract

Identifying and mitigating bias in deep learning algorithms has gained significant popularity in the past few years due to its impact on the society. Researchers argue that models trained on balanced datasets with good representation provide equal and unbiased performance across subgroups. However, \textit{can seemingly unbiased pre-trained model become biased when input data undergoes certain distortions?} For the first time, we attempt to answer this question in the context of face recognition. We provide a systematic analysis to evaluate the performance of four state-of-the-art deep face recognition models in the presence of image distortions across different \textit{gender} and \textit{race} subgroups. We have observed that image distortions have a relationship with the performance gap of the model across different subgroups.