| dc.description.abstract | The rapid advancement of affective computing has opened new avenues for supporting early
childhood development through intelligent systems. However, traditional facial expression
recognition (FER) models, primarily trained on adult datasets, often fail to generalize to the unique
facial structures and nuanced emotional displays of infants and toddlers. This research presents an
integrated AI-driven framework designed to provide personalized toy recommendations based on
real-time infant emotion detection. The system utilizes a specialized pipeline incorporating MTCNN
for precise facial alignment and a comparative analysis of high-performance architectures, including
EfficientNet-B0, DenseNet121, ResNet50, and YOLO for robust feature extraction and classification.
To address the critical scarcity of open-access data for the 0–1.5 year age group, a domain-specific
dataset was curated to enhance model reliability in detecting subtle affective states. Beyond technical
classification, the research introduces a scalable mobile application that bridges the gap between
emotion analysis and consumer intelligence by offering mood-aligned toy suggestions with integrated
price-tracking features. Experimental results indicate that our approach significantly provides a high
degree of accuracy in resource-constrained environments. This study contributes a foundational
methodology for infant-centric AI applications, offering a practical tool to foster emotional growth
and personalized play experiences on a global scale. | en_US |
