Face recognition (FR) models are vulnerable to performance variations across demographic groups. The causes for these performance differences are unclear due to the highly complex deep learning-based structure of face recognition models. Several works aimed at exploring possible roots of gender and ethnicity bias, identifying semantic reasons such as hairstyle, make-up, or facial hair as possible sources. Motivated by recent discoveries of the importance of frequency patterns in convolutional neural networks, we explain bias in face recognition using state-of-the-art frequency-based explanations. Our extensive results show that different frequencies are important to FR models depending on the ethnicity of the samples.
@article{arxiv.2501.16896,
title = {Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain},
author = {Marco Huber and Fadi Boutros and Naser Damer},
journal= {arXiv preprint arXiv:2501.16896},
year = {2025}
}