EXPLAINABLE AND RESILIENT FACE RECOGNITION SYSTEMS FOR INCLUSIVE ECONOMIC GROWTH

Abstract

Face recognition systems are increasingly used in digital identity, financial services, and public-sector applications, yet many existing models remain opaque and vulnerable to bias, exclusion, and performance degradation under real-world conditions. This paper presents an integrated framework for explainable and resilient face recognition that combines robust deep feature extraction, post-hoc explainable AI techniques, and resilience strategies such as data augmentation, adversarial training, and domain generalization. An evaluation protocol is proposed to assess recognition accuracy, robustness to perturbations and distributional shifts, interpretability, and fairness across demographic groups. The framework is intended to support the development of trustworthy face recognition systems that reduce exclusion errors, improve transparency, and advance inclusive digital and economic participation in heterogeneous, data-constrained environments.

Keywords: Face recognition, Explainable AI, Resilience, Fairness, Inclusive economic growth