EquiAI: A Vision Foundation Model for Fair and Robust Cross-Demographic Remote Identity Verification

Abstract

Remote identity verification has become a critical component of autonomous agents deployed in smart healthcare, intelligent transportation, digital finance, and secure access control systems. Recent advances in Vision Foundation Models (VFMs) have significantly improved facial representation learning; however, their deployment remains challenged by demographic bias, which leads to inconsistent verification performance across different age groups, genders, ethnicities, and skin tones. Such disparities reduce the reliability, fairness, and trustworthiness of AI-enabled biometric systems, particularly in safety-critical applications where accurate identity verification is essential. This study proposes EquiAI, a robust fairness-aware remote identity verification framework that integrates a pretrained Vision Foundation Model (DINOv2), fairness-aware representation learning, adaptive feature alignment, presentation attack detection, and explainable artificial intelligence into a unified architecture. The proposed framework employs transformer-based feature extraction to learn generalized facial embeddings while minimizing demographic disparities and enhancing resistance against spoofing attacks. Experimental evaluation demonstrates that EquiAI achieves a verification accuracy of 98.21%, an F1-score of 97.94%, an ROC-AUC of 0.991, and an Equal Error Rate of 1.82%, while maintaining balanced performance across diverse demographic groups and supporting real-time inference. These findings demonstrate that integrating Vision Foundation Models with fairness-aware optimization and explainable AI substantially improves the robustness, equity, and practical deployment of remote identity verification systems, providing a scalable foundation for trustworthy autonomous agent authentication in real-world environments.