<![CDATA[Background: Facial expressions are a primary form of nonverbal communication for toddlers whose verbal abilities are still developing. Specific background: Advances in computer vision and deep learning have enabled real-time facial expression detection; however, most existing systems are designed using adult facial datasets and pretrained models. Knowledge gap: Research focusing on toddler facial expression detection using models trained exclusively on toddler data without pretrained weights remains limited. Aims: This study applies YOLOv8 to detect happy, sad, and neutral facial expressions of toddlers in real time using a model trained from scratch. Results: The proposed system achieved an average detection accuracy of 86%, with precision of 0.944, recall of 0.933, and mean Average Precision at 0.5 of 0.966, demonstrating stable real-time performance under varying lighting conditions. Novelty: The study demonstrates that YOLOv8 can learn toddler-specific facial expression patterns without relying on pretrained weights derived from adult facial data. Implications: The findings indicate the feasibility of deploying real-time toddler facial expression detection systems to support emotional monitoring in childcare and early education environments. Highlights Real-time detection of toddler facial expressions using YOLOv8. Model training conducted entirely from scratch using toddler facial datasets. Consistent detection performance observed in natural and varied environments. Keywords Facial Expression Detection, Toddler, YOLOv8, Computer Vision, Real-Time System]]>
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