Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children
Pavel Kozlov, Alisher Akram, Pakizar Shamoi

TL;DR
This paper introduces a fuzzy-based framework for recognizing and analyzing children's emotions during gameplay using audio-visual data, aiming to improve game design and parental oversight.
Contribution
It presents a novel fuzzy inference system for integrating audio and video emotion recognition in children's computer games, including emotion stability and diversity analysis.
Findings
Promising preliminary results on three different game types.
Effective detection of facial and audio emotions during gameplay.
Potential for enhancing child-oriented game development.
Abstract
Computer games are widespread nowadays and enjoyed by people of all ages. But when it comes to kids, playing these games can be more than just fun, it is a way for them to develop important skills and build emotional intelligence. Facial expressions and sounds that kids produce during gameplay reflect their feelings, thoughts, and moods. In this paper, we propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. Our focus lies within the specific context of computer games tailored for children, aiming to enhance their overall user experience. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets. Next, a fuzzy inference system is…
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Taxonomy
TopicsEmotion and Mood Recognition
MethodsFocus
