Unveiling Gender Effects in Gait Recognition Using Conditional-Matched Bootstrap Analysis

While biases such as gender, race, and age have been closely examined in biometric recognition, especially in face and fingerprint traits, their exploration in gait-based recognition is lacking, except for one study. We formulate conditional-matched bootstrap analysis to control for confounding covariates like clothing style, height, and walking speed. The goal is to isolate genuine gender effects on gait recognition. We delve into gender-based disparities in gait recognition by using several state-of-the-art gait recognition methodologies - GaitSet, GaitPart, and GaitGL. For our analysis, the widely-referenced OU-MVLP dataset served as our foundation, which we enhanced with annotations about clothing style, body height, and walking speed. The results were illuminating. We observed a disparity in recognition performance across genders on the original dataset, with recognition for females higher than for males. However, after controlling for covariate distributions using conditional-matched bootstrap analysis, the gap was reduced, with clothing type emerging as the most significant contributor. Code available at https://github.com/azimIbragimov/gait-gender