Effectiveness of Using Artificial Intelligence for Early Child Development Screening

Gau, Michael-Lian and Ting, Huong-Yong and Toh, Teck-Hock and Wong, Pui-Ying and Woo, Pei Jun * and Wo, Su-Woan and Tan, Gek-Ling (2023) Effectiveness of Using Artificial Intelligence for Early Child Development Screening. Green Intelligent Systems and Applications, 3 (1). pp. 1-13. ISSN 2809-1116

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Abstract

This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.

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