DOI : https://doi.org/10.62527/ijasce.6.3.213

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
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How to cite (IJASEIT) :
Santoso , H., Ferreira Soares, G., & Angelo, C. M. (2024). 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), 152–157. https://doi.org/10.62527/ijasce.6.3.213
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.

D. Piontkowski and R. Calfee, “Child Emotion Detection Through Facial Expression Recognition Using Machine Learning,” Attention and Cognitive Development, pp. 297–329, 2023, doi: 10.1007/978-1-4613-2985-5_11.

U. Laraib, A. Shaukat, R. A. Khan, Z. Mustansar, M. U. Akram, and U. Asgher, “Recognition of Children’s Facial Expressions Using Deep Learned Features,” Electronics (Switzerland), vol. 12, no. 11, Jun. 2023, doi: 10.3390/electronics12112416.

E. Guo, “A Review of the Facial Emotion Recognition in Children with Autism,” in Proceedings of the 2022 5th International Conference on Humanities Education and Social Sciences (ICHESS 2022), Atlantis Press SARL, 2022, pp. 2411–2418. doi: 10.2991/978-2-494069-89-3_277.

J. G. Negrão et al., “The Child Emotion Facial Expression Set: A Database for Emotion Recognition in Children,” Front Psychol, vol. 12, Apr. 2021, doi: 10.3389/fpsyg.2021.666245.

Y. Wang, Q. Luo, Y. Zhang, and K. Zhao, “Synchrony or asynchrony: development of facial expression recognition from childhood to adolescence based on large-scale evidence,” Front Psychol, vol. 15, 2024, doi: 10.3389/fpsyg.2024.1379652.

Y. Ihza and D. Lelono, “Face Expression Classification in Children Using CNN,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 16, no. 2, p. 159, Apr. 2022, doi: 10.22146/ijccs.72493.

M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Jan. 01, 2023, KeAi Communications Co. doi: 10.1016/j.cogr.2023.04.001.

M. M. Taye, “Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions,” May 01, 2023, MDPI. doi: 10.3390/computers12050091.

K. Reghunandanan, V. S. Lakshmi, R. Raj, K. Viswanath, C. Davis, and R. Chandramohanadas, “A Convolutional Neural Network- Based Deep Learning To Detect Reticulocytes From Human Peripheral Blood,” Intell Based Med, vol. 10, Jan. 2024, doi: 10.1016/j.ibmed.2024.100175.

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif Intell Rev, vol. 57, no. 4, Apr. 2024, doi: 10.1007/s10462-024-10721-6.

J. Serey et al., “Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research,” Feb. 01, 2023, MDPI. doi: 10.3390/sym15020535.

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif Intell Rev, vol. 57, no. 4, Apr. 2024, doi: 10.1007/s10462-024-10721-6.

M. Ali Sultan et al., “Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet,” International Journal of Informatics and Computation (IJICOM), vol. 6, no. 1, 2024, doi: 10.35842/ijicom.

H. Santoso, I. Hanif, and H. Magdalena, “A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction,” vol. 8, no. 2, 2024, doi: 10.62527/joiv.8.2.1943.

E. S. Agung, A. P. Rifai, and T. Wijayanto, “Image-based facial emotion recognition using convolutional neural network on emognition dataset,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-65276-x.

Y. Permanasari, B. N. Ruchjana, S. Hadi, and J. Rejito, “Innovative Region Convolutional Neural Network Algorithm for Object Identification,” Dec. 01, 2022, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/joitmc8040182.

R. Zimmer, M. Sobral, and H. Azevedo, “Hybrid Models for Facial Emotion Recognition in Children,” Aug. 2023, [Online]. Available: http://arxiv.org/abs/2308.12547

Y.-J. Zhang, J.-Y. Chen, and Z.-M. Lu, “Face anti-spoofing detection based on color texture structure analysis,” Taiwan Ubiquitous Information, vol. 7, no. 2, 2022.

P. Jaswanth, P. Y. chowdary, and M. V. S. Ramprasad, “Deep learning based intelligent system for robust face spoofing detection using texture feature measurement,” Measurement: Sensors, vol. 29, Oct. 2023, doi: 10.1016/j.measen.2023.100868.

C.-Y. Weng, “Land-Use Classification via Transfer Learning with a Deep Convolutional Neural Network,” Journal of Intelligent Learning Systems and Applications, vol. 14, no. 02, pp. 15–23, 2022, doi: 10.4236/jilsa.2022.142002.