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![]() Title:Analyzing Emotion Patterns in Gaming Using CNNs on Facial and Vocal Features Authors:Fahad Khan Raj, Rakib Hasan Rahad, Monthasir Delwar Afnan, Akhlak Ur Rahman, Md. Samir Uddin Ahmed, Md. Khalilur Rhaman and Md. Golam Rabiul Alam Conference:STI 2025 Tags:Behavior analysis, DL Algorithms, Facial Expression Recognition, ML Algorithms, Multimodal Emotion Recognition, Speech Emotion Recognition and Video games Abstract: People of different ages frequently use technology for leisure activities, and gaming is a common pastime for many. However, playing video games may cause significant changes in behavior, both positive and negative. Research about those changes has been ongoing for a long time. Most of the research was conducted using sophisticated medical environments. We propose a multimodal CNN-based approach to observe emotional changes in players using facial and speech cues extracted from gameplay video frames. A vast amount of YouTube videos was collected from different online gaming streamers and then image and audio datasets comprising hundreds of those videos. Utilizing Facial Expression Recognition (FER) and Speech Emotion Recognition (SER) methodologies, our objective was to identify patterns of behavioral changes during gaming sessions and longitudinally. Multiple models were employed for both SER and FER. For FER, DenseNet121 was fine-tuned and achieved the best performance, and for SER, a custom CNN architecture, specifically optimized for speech features like MFCCs and spectrograms, was developed and outperformed other SER models. In our research, we established the effectiveness of our approach in discerning patterns associated with behavioral changes. Analyzing Emotion Patterns in Gaming Using CNNs on Facial and Vocal Features ![]() Analyzing Emotion Patterns in Gaming Using CNNs on Facial and Vocal Features | ||||
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