DETECTION OF CHILDREN'S FACIAL EXPRESSIONS ON THE EFFECTS OF PLAYING GAMES USING CNN ALGORITHM

SOARES, GENOVEVA FERREIRA (2024) DETECTION OF CHILDREN'S FACIAL EXPRESSIONS ON THE EFFECTS OF PLAYING GAMES USING CNN ALGORITHM. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This thesis explores into 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. The study intends to assess CNN's accuracy in detecting these five basic emotions among children aged 6 to 13 also with Kaggle dataset during gaming sessions by studying facial expressions, notably those suggestive of 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. 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. children's emotional states, paving 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 experience but also emphasizes the need of developing emotionally resonant connection with digital settings aimed and youngest. Keywords: Children, Facial expression Recognition, Gaming, Convolutional Neural Network (CNN), Emotional Analysis

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 109
Call Number:	SIK/15/24/075
NIM/NIDN Creators:	41520010149
Uncontrolled Keywords:	Children, Facial expression Recognition, Gaming, Convolutional Neural Network (CNN), Emotional Analysis
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 070 Documentary Media, Educational Media, News Media, Journalism, Publishing/Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.1-070.9 Standard Subdivisions of Documentary Media, Educational Media, News Media, Journalism, Publishing/Subdivisi Standar Dari Media Dokumenter, Media Pendidikan, Media Berita, Jurnalisme, Penerbitan > 070.4 Journalism/Jurnalisme, Jurnalistik, Pers > 070.48 Journalism Directed to Special Groups/Jurnalistik Disutradarai oleh Kelompok Khusus > 070.483 Groups by Age and Sex/Kelompok Menurut Usia dan Jenis Kelamin > 070.4832 Children/Anak-anak 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	16 Jul 2024 06:25
Last Modified:	16 Jul 2024 06:25
URI:	http://repository.mercubuana.ac.id/id/eprint/89581

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