Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding

📝 Original Info

• Title: Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding
• ArXiv ID: 2512.08981
• Date: 2025-12-05
• Authors: Tahar Chettaoui, Naser Damer, Fadi Boutros

📝 Abstract

Face recognition (FR) systems are often prone to demographic biases, partially due to the entanglement of demographic-specific information with identity-relevant features in facial embeddings. This bias is extremely critical in large multicultural cities, especially where biometrics play a major role in smart city infrastructure. The entanglement can cause demographic attributes to overshadow identity cues in the embedding space, resulting in disparities in verification performance across different demographic groups. To address this issue, we propose a novel strategy, Unified Text-Image Embedding (UTIE), which aims to induce demographic ambiguity in face embeddings by enriching them with information related to other demographic groups. This encourages face embeddings to emphasize identity-relevant features and thus promotes fairer verification performance across groups. UTIE leverages the zero-shot capabilities and cross-modal semantic alignment of Vision-Language Models (VLMs). Given that VLMs are naturally trained to align visual and textual representations, we enrich the facial embeddings of each demographic group with text-derived demographic features extracted from other demographic groups. This enc...

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