Publications
Frequency Matters: Explaining Biases of Face Recognition in the Frequency Domain
| Author | Huber, Marco; Boutros, Fadi; Damer, Naser |
|---|---|
| Date | 2025 |
| Type | Conference Paper |
| Abstract | 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. |
| Conference | European Conference on Computer Vision 2024 |
| Project | Next Generation Biometric Systems |
| Url | https://publica.fraunhofer.de/handle/publica/488000 |
