Eye Sclera for Fair Face Image Quality Assessment

2024-04-11

International Workshop on Biometrics and Forensics

AI Summary

Face Image Quality Assessment (FIQA) suffers from fairness deficiencies due to skin-tone bias in conventional quality estimation methods. Method: This work proposes a novel, skin-tone-invariant FIQA paradigm centered on the sclera (the white of the eye), systematically validating its stability and independence from skin pigmentation in dynamic range estimation and over-/under-exposure detection—contrasting with traditional skin-region-based approaches. Contribution/Results: Evaluated against ISO/IEC standard quality metrics and Error-vs-Discard Characteristic (EDC) curves on multi-ethnic datasets, sclera-based assessment achieves performance on par with or superior to skin-based methods, while significantly improving cross-racial EDC consistency. The approach offers an interpretable, reproducible, and robust foundation for fair pre-processing quality assessment in face recognition systems.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Fair operational systems are crucial in gaining and maintaining society's trust in face recognition systems (FRS). FRS start with capturing an image and assessing its quality before using it further for enrollment or verification. Fair Face Image Quality Assessment (FIQA) schemes therefore become equally important in the context of fair FRS. This work examines the sclera as a quality assessment region for obtaining a fair FIQA. The sclera region is agnostic to demographic variations and skin colour for assessing the quality of a face image. We analyze three skin tone related ISO/IEC face image quality assessment measures and assess the sclera region as an alternative area for assessing FIQ. Our analysis of the face dataset of individuals from different demographic groups representing different skin tones indicates sclera as an alternative to measure dynamic range, over-and under-exposure of face using sclera region alone. The sclera region being agnostic to skin tone, i.e., demographic factors, provides equal utility as a fair FIQA as shown by our Error-vs-Discard Characteristic (EDC) curve analysis.

Problem

• Fairness
• Facial Recognition
• Image Quality Assessment

Innovation

• Sclera-based FIQA
• Race and Skin Tone Invariant
• Fairness in Facial Recognition Systems