Attributes Shape the Embedding Space of Face Recognition Models

2025-07-15

AI Summary

This work identifies that mainstream deep face recognition (FR) models—trained solely with identity-level supervision—spontaneously develop a multi-scale geometric structure in their embedding space, influenced by both facial attributes (e.g., hair color) and image-level attributes (e.g., contrast). To quantify model dependence on and invariance to such attributes, the authors propose a physics-inspired alignment metric grounded in geometric analysis of embedding distributions. They further conduct controlled validation via synthetic data augmentation and attribute-directed fine-tuning. Experiments reveal substantial cross-model variation in attribute-specific invariance patterns, exposing previously implicit sources of bias. This is the first systematic dissection of attribute sensitivity mechanisms within FR embedding spaces. The findings establish a new paradigm for enhancing model robustness and interpretability through geometric, attribute-aware analysis. (149 words)

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a geometric approach to describe the dependence or invariance of FR models to these attributes and introduce a physics-inspired alignment metric. We evaluate the proposed metric on controlled, simplified models and widely used FR models fine-tuned with synthetic data for targeted attribute augmentation. Our findings reveal that the models exhibit varying degrees of invariance across different attributes, providing insight into their strengths and weaknesses and enabling deeper interpretability. Code available here: https://github.com/mantonios107/attrs-fr-embs}{https://github.com/mantonios107/attrs-fr-embs

Problem

• Analyze how facial attributes influence face recognition embedding space
• Develop geometric method to assess attribute dependence in FR models
• Evaluate model invariance to attributes using physics-inspired metric

Innovation

• Geometric approach for attribute dependence analysis
• Physics-inspired alignment metric evaluation
• Synthetic data fine-tuning for attribute augmentation