A Survey on AI-Based Child Detection and Safety Mechanisms in Smartphone

Abstract

The proliferation of smartphones among children has sparked widespread concern over digital addiction, unsupervised content access, and mental health consequences. This survey paper presents a comprehensive review of artificial intelligence (AI)-based approaches for child detection and safety regulation in mobile environments. We explore age estimation through facial recognition, behavioral analytics, and context-based models with an emphasis on on-device processing for privacy preservation. We introduce a classification taxonomy, analyze over several papers, identify gaps in real-time accuracy, ethical design, and dataset limitations, and propose future research directions. Our study offers a roadmap for the development of adaptive, AI-driven child safety tools aligned with digital well-being standards.