Survey highlights AI-based child detection and safety mechanisms, indicating gaps in real-time accuracy.
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.
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Gandhadamatha et al. (2025) studied this question.
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