Emotion Recognition System for Game Addiction Users Using FACS and Image Feature Extraction on Android

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Authors

  • Roslinda Tanjung Universitas Muhammadiyah Sumatera Utara
  • Yohanni Syahra Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.56211/tsabit76

Keywords:

Emotion Recognition;; FACS;; Android;; Image Feature Extraction;; Game Addiction;

Abstract

Purpose: Online game addiction negatively impacts users’ mental health and social behavior. This study aims to develop an Android-based emotion recognition system using Facial Action Coding System (FACS) with image feature extraction to detect users’ emotions in real-time.

Design/Methods/Approach: The system integrates OpenCV for face detection, dlib for facial landmark identification, and TensorFlow Lite for emotion classification. Testing was conducted through black box testing, confusion matrix evaluation, and performance analysis.

Findings/Results: The system successfully recognized basic negative emotions (anger, frustration, sadness, displeasure) with adequate accuracy (87%) and operated effectively on Android devices.

Conclusions: Integrating FACS with image feature extraction provides a non-intrusive solution to help users recognize and manage emotions, contributing to digital mental health research and practice.

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References

[1] R. Sari and D. Astuti, “Game Addiction and Mental Health,” Journal of Psychology, vol. 12, no. 2, pp. 45–56, 2021.

[2] J. Kramer, “Digital Emotion Recognition Systems,” in Proc. Int. Conf. Artificial Intelligence, 2025, pp. 233–240.

[3] P. Gupta, A. Sharma, and R. Singh, “Facial Action Coding System in AI Applications,” IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 567–579, 2023.

[4] K. Niinuma, H. Takahashi, and Y. Sato, “Standardization of Facial Action Units for Emotion Recognition,” Journal of Behavioral Science, vol. 8, no. 1, pp. 22–34, 2021.

[5] H. Surbakti, C. Yusainy, and R. Pratama, “Emotion Recognition for Digital Health Applications,” International Journal of Computer Vision, vol. 11, no. 4, pp. 112–124, 2023.

[6] A. Mahmoud, S. Ali, and M. Hassan, “Advances in Facial Expression Analysis Using FACS,” Pattern Recognition Letters, vol. 165, pp. 45–59, 2025.

[7] C. Yusainy and S. Walinono, “Emotion Regulation in Digital Contexts,” Asian Journal of Psychology and Technology, vol. 7, no. 2, pp. 88–97, 2025.

[8] R. Gustiana, M. Martadi, and S. Mega, “Emotion Recognition through Facial and Speech Signals,” International Journal of Human-Computer Interaction, vol. 40, no. 5, pp. 321–334, 2024.

[9] R. Meiyanti and M. Sandy, “Feature Extraction in Speech Emotion Recognition,” Journal of Information Technology Research, vol. 9, no. 3, pp. 77–85, 2021.

[10] X. Yu, L. Zhang, and F. Wang, “Deep Learning Approaches for Facial Action Unit Detection,” IEEE Transactions on Multimedia, vol. 26, no. 2, pp. 345–356, 2024.

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Published

2026-07-14

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Issue

Vol. 3 No. 1 (2026): June Edition

Section

Articles

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Copyright (c) 2026 Roslinda Tanjung, Yohanni Syahra

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