Emotional Facial Expression Detection using YOLOv8
Received: 18 July 2024 | Revised: 27 July 2024 and 31 July 2024 | Accepted: 4 August 2024 | Online: 10 August 2024
Corresponding author: Aadil Alshammari
Abstract
Emotional facial expression detection is a critical component with applications ranging from human-computer interaction to psychological research. This study presents an approach to emotion detection using the state-of-the-art YOLOv8 framework, a Convolutional Neural Network (CNN) designed for object detection tasks. This study utilizes a dataset comprising 2,353 images categorized into seven distinct emotional expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. The findings suggest that the YOLOv8 framework is a promising tool for emotional facial expression detection, with a potential for further enhancement through dataset augmentation. This research demonstrates the feasibility and effectiveness of using advanced CNN architectures for emotion recognition tasks.
Keywords:
emotion detection, deep learning, YOLOv8Downloads
References
G. Jocher, A. Chaurasia, and J. Qiu, "Ultralytics YOLO." Jan. 2023, [Online]. Available: https://github.com/ultralytics/ultralytics.
A. M. Islam, "Exploring Convolutional Neural Networks for Facial Expression Recognition: A Comprehensive Survey," Global Mainstream Journal of Innovation, Engineering & Emerging Technology, vol. 3, no. 02, pp. 14–26, Mar. 2024.
B. Ma et al., "Distracted Driving Behavior and Driver’s Emotion Detection Based on Improved YOLOv8 With Attention Mechanism," IEEE Access, vol. 12, pp. 37983–37994, 2024.
P. Agarwal, "Real-time facial emotion recognition web application," Ph.D. dissertation, International Institute of Information Technology, Hyderabad, India, 2024.
H. Ma, S. Lei, T. Celik, and H. C. Li, "FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space." arXiv, May 09, 2024.
W. Ismaiel, A. Alhalangy, A. O. Y. Mohamed, and A. I. A. Musa, "Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13757–13764, Apr. 2024.
H. M. Al-Dabbas, R. A. Azeez, and A. E. Ali, "Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13706–13713, Apr. 2024.
M. A. Hasan and A. H. Lazem, "Facial human emotion recognition by using YOLO faces detection algorithm," Central Asian Journal of Mathematical Theory and Computer Sciences, vol. 4, no. 10, pp. 1–11, Oct. 2023.
T. Saidani, "Deep Learning Approach: YOLOv5-based Custom Object Detection," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023.
M. Wang, P. Tan, X. Zhang, Y. Kang, C. Jin, and J. Cao, "Facial expression recognition based on CNN," Journal of Physics: Conference Series, vol. 1601, no. 5, Dec. 2020, Art. no. 052027.
T. Connie, M. Al-Shabi, W. P. Cheah, and M. Goh, "Facial Expression Recognition Using a Hybrid CNN–SIFT Aggregator," in Multi-disciplinary Trends in Artificial Intelligence, Gadong, Brunei, 2017, pp. 139–149.
K. Mohan, A. Seal, O. Krejcar, and A. Yazidi, "Facial Expression Recognition Using Local Gravitational Force Descriptor-Based Deep Convolution Neural Networks," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021.
M. Shin, M. Kim, and D. S. Kwon, "Baseline CNN structure analysis for facial expression recognition," in 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, USA, Dec. 2016, pp. 724–729.
D. Hamester, P. Barros, and S. Wermter, "Face expression recognition with a 2-channel Convolutional Neural Network," in 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, Jul. 2015, pp. 1–8.
K. Liu, M. Zhang, and Z. Pan, "Facial Expression Recognition with CNN Ensemble," in 2016 International Conference on Cyberworlds (CW), Chongqing, China, Sep. 2016, pp. 163–166.
I. Talegaonkar, K. Joshi, S. Valunj, R. Kohok, and A. Kulkarni, "Real Time Facial Expression Recognition using Deep Learning," in Proceedings of International Conference on Communication and Information Processing (ICCIP), May 2019.
J. Li, K. Jin, D. Zhou, N. Kubota, and Z. Ju, "Attention mechanism-based CNN for facial expression recognition," Neurocomputing, vol. 411, pp. 340–350, Oct. 2020.
M. A. Ozdemir, B. Elagoz, A. Alaybeyoglu, R. Sadighzadeh, and A. Akan, "Real Time Emotion Recognition from Facial Expressions Using CNN Architecture," in 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey, Jul. 2019, pp. 1–4.
"Emotion Detection Dataset." Roboflow Universe, 2022, [Online]. Available: https://universe.roboflow.com/lyanhvini/emotion-detection-a5i5h.
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