December 4, 2025Applied System InnovationOpen Access

Enhancing the Effectiveness of Juvenile Protection: Deep Learning-Based Facial Age Estimation via JPSD Dataset Construction and YOLO-ResNet50

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Facial age estimation framework improves supervision in venues for youths, highlighting juvenile protection implications.

Achieved Recall of 95.17% for over-18 group using convolutional neural networks in age classification, indicating model effectiveness.
Baseline model comparisons showed YOLOv11 outperforms others in facial detection for juvenile settings.
Constructed JPSD Dataset integrates five public datasets, enabling more targeted age-specific detection for juveniles.
Framework highlights need for stringent human supervision when implementing age estimation technology for safety.

Wu et al. (2025) studied this question.

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