Detection of Children's Facial Expressions on The Effects of Playing Games Using CNN Algorithm

Hadi Santoso (1), Genoveva Ferreira Soares (2), Cristopher Marco Angelo (3)
(1) Universitas Mercu Buana Jl. Meruya Selatan No.1, Kembangan, West Jakarta, Jakarta 11610, Indonesia
(2) Universitas Mercu Buana Jl. Meruya Selatan No.1, Kembangan, West Jakarta, Jakarta 11610, Indonesia
(3) Universitas Mercu Buana Jl. Meruya Selatan No.1, Kembangan, West Jakarta, Jakarta 11610, Indonesia
How to cite (IJASEIT) :
Santoso , H., Ferreira Soares, G., & Angelo, C. M. (2025). Detection of Children’s Facial Expressions on The Effects of Playing Games Using CNN Algorithm. International Journal of Advanced Science Computing and Engineering, 6(3). Retrieved from https://ijasce.org/index.php/IJASCE/article/view/213
Citation Format :
This thesis explores the use of Convolutional Neural Network (CNN) algorithms for the aim of recognizing children's facial expressions during gaming activities, with a focus on understanding the emotional consequences of gaming. This study intends to assess CNN's accuracy in detecting the five basic emotions among children between the age of 6 to 13 using the Kaggle dataset during gaming sessions by studying facial expressions, notably those that suggest anger, happiness, sadness, fear, surprise, and disgust. The methodology consists of numerous processes, including data collection, preprocessing, augmentation, model training, and evaluation, with the overarching goal of identifying patterns and trends in children's emotional responses to gaming. The study uses CNN algorithms to build strong models capable of accurately recognizing and categorizing children's facial expressions, providing significant insights into the emotional dynamics inherent in gaming experiences. The methodology consists of numerous processes, including data collection, preprocessing, augmentation, model training, and evaluation, with the overarching goal of identifying patterns and trends in children's emotional responses to gaming. This study uses CNN algorithms to build strong models capable of accurately recognizing and categorizing children's facial expressions, providing significant insights into the emotional dynamics inherent in gaming experiences. Children's emotional states pave the door for the creation of more compassionate and engaging gaming experiences that are suited to children's emotional needs. This study not only influences the design and implementation of gaming experiences but also emphasizes the need of developing emotionally resonant connections with digital settings aimed towards children.

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