GenderRobustness: Robustness of Gender Detection in Facial Recognition Systems with variation in Image Properties

TL;DR

This paper investigates the robustness of gender detection in facial recognition systems, focusing on how variations in image properties affect bias and accuracy, highlighting the importance of minimizing gender bias in widespread AI applications.

Contribution

It introduces a comprehensive analysis of gender detection robustness against image variations, emphasizing the need for bias mitigation in facial recognition systems.

Findings

Bias persists in gender detection across various image conditions

Image property variations significantly impact gender recognition accuracy

Highlighting the necessity for robust and fair facial recognition algorithms

Abstract

In recent times, there have been increasing accusations on artificial intelligence systems and algorithms of computer vision of possessing implicit biases. Even though these conversations are more prevalent now and systems are improving by performing extensive testing and broadening their horizon, biases still do exist. One such class of systems where bias is said to exist is facial recognition systems, where bias has been observed on the basis of gender, ethnicity, skin tone and other facial attributes. This is even more disturbing, given the fact that these systems are used in practically every sector of the industries today. From as critical as criminal identification to as simple as getting your attendance registered, these systems have gained a huge market, especially in recent years. That in itself is a good enough reason for developers of these systems to ensure that the bias is kept to a bare minimum or ideally non-existent, to avoid major issues like favoring a particular gender, race, or class of people or rather making a class of people susceptible to false accusations due to inability of these systems to correctly recognize those people.