A Comparative Study of CNN, RNN-LSTM, and Transfer Learning Models for Facial Emotion Recognition in context of gaming

Gole, Ayush Vitthal (2024) A Comparative Study of CNN, RNN-LSTM, and Transfer Learning Models for Facial Emotion Recognition in context of gaming. Masters thesis, Dublin, National College of Ireland.

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Abstract

The boomed growth of Esports industry, highlights intense competition within industry and gameplay. This underscores the need of adaptive gaming and deeper understanding of player emotions with gaming context. By analysing gamers emotions with game context, we aim to develop base for framework for tailoring gaming experiences according to gamers. Leveraging different neural network models and methods like CNN , transfer learning VGG16 and RNN-LSTM to compare and find more suitable one for gaming emotion detection is prime goal of this research. This research shows initial preprocessing standards needed, data collection and standardization along side method, models suitable for implementation. This research will contribute to advancement to adaptive gaming by providing insights of research models and implementation.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Vamadevan, Arundev UNSPECIFIED
Uncontrolled Keywords:	Adaptive gaming; CNN; Emotion analysis; RNN-LSTM; Transfer learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science G Geography. Anthropology. Recreation > GV Recreation Leisure > Games and Amusements > Computer Games. Video Games. B Philosophy. Psychology. Religion > Psychology > Emotions Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	19 Jun 2025 15:39
Last Modified:	19 Jun 2025 15:39
URI:	https://norma.ncirl.ie/id/eprint/7946

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