Facial Human Emotion Recognition by Using YOLO Faces Detection Algorithm Pengenalan Emosi Wajah Manusia dengan Menggunakan Algoritma Deteksi Wajah YOLO
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Published:
Nov 30, 2023
Keywords:
Facial Emotion Recognition, Emotion Classification, Feature Extraction, Convolutional Neural Networks, Deep Learning
Main Article Content
Abstract
Deep emotions have gained importance recently because they constitute a form of interpersonal nonverbal communication that has been demonstrated and used in a variety of real-world contexts, including human-machine interactions, safety, and health. The best elements of a human face must be extracted in order to forecast the proper emotion expression, making this method extremely difficult. In this work, we provide a brand-new structural model to forecast human emotion on the face. The human face is found using the YOLO faces detection technique, and its attributes are extracted. These features then help to classify the face image into one of the seven emotions: natural, happy, sad, angry, surprised, fear, or disgust. The experiment demonstrated the robustness and speed of the suggested structure. This paper made use of the FER2013 dataset. The experimental findings demonstrated that the proposed system's accuracy was 94%.
Article Details
How to Cite
Hasan, M. A. (2023). Facial Human Emotion Recognition by Using YOLO Faces Detection Algorithm. JOINCS (Journal of Informatics, Network, and Computer Science), 6(2), 32 - 38. https://doi.org/10.21070/joincs.v6i2.1629
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Articles
Copyright (c) 2023 Mustafa Asaad Hasan, Ali Hussein Lazem, Mohamed Ayad Alkhafaji, Hazeem B. Taher
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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3. Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788. 2016.
4. Fathallah, Abir, Lotfi Abdi, and Ali Douik. "Facial expression recognition via deep learning." In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 745-750. IEEE, 2017.
5. Mehendale, Ninad. "Facial emotion recognition using convolutional neural networks (FERC)." SN Applied Sciences 2, no. 3 (2020): 446.
6. Giannopoulos, Panagiotis, Isidoros Perikos, and Ioannis Hatzilygeroudis. "Deep learning approaches for facial emotion recognition: A case study on FER-2013." Advances in Hybridization of Intelligent Methods: Models, Systems and Applications (2018): 1-16.
7. Taher, Hazeem B., Kadhim M. Hashim, and Atheer Yousif Oudah. "Adaptive hybrid technique for face recognition." Periodicals of Engineering and Natural Sciences 7, no. 2 (2019): 818-823.
8. Hu, Guosheng, Li Liu, Yang Yuan, Zehao Yu, Yang Hua, Zhihong Zhang, Fumin Shen et al. "Deep multi-task learning to recognise subtle facial expressions of mental states." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 103-119. 2018.
9. Zhang, Shifeng, Xiaobo Wang, Ajian Liu, Chenxu Zhao, Jun Wan, Sergio Escalera, Hailin Shi, Zezheng Wang, and Stan Z. Li. "A dataset and benchmark for large-scale multi-modal face anti-spoofing." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 919-928. 2019.
10. Liu, Jingtuo, Yafeng Deng, Tao Bai, Zhengping Wei, and Chang Huang. "Targeting ultimate accuracy: Face recognition via deep embedding." arXiv preprint arXiv:1506.07310 (2015).
11. Ghadekar, Premanand P., Hanan Ali Alrikabi, and Nilkanth B. Chopade. "Efficient face and facial expression recognition model." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA), pp. 1-8. IEEE, 2016.
12. Garg, Ankit, and Ashish Negi. "A Survey on Content Aware Image Resizing Methods." KSII Transactions on Internet & Information Systems 14, no. 7 (2020).
13. Fan, Linwei, Fan Zhang, Hui Fan, and Caiming Zhang. "Brief review of image denoising techniques." Visual Computing for Industry, Biomedicine, and Art 2 (2019): 1-12.
14. Qi, Yunliang, Zhen Yang, Wenhao Sun, Meng Lou, Jing Lian, Wenwei Zhao, Xiangyu Deng, and Yide Ma. "A comprehensive overview of image enhancement techniques." Archives of Computational Methods in Engineering (2021): 1-25.
15. Yu, Tao, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, and Sen Liu. "Region normalization for image inpainting." In Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 07, pp. 12733-12740. 2020.
16. Minaee, Shervin, Yuri Boykov, Fatih Porikli, Antonio Plaza, Nasser Kehtarnavaz, and Demetri Terzopoulos. "Image segmentation using deep learning: A survey." IEEE transactions on pattern analysis and machine intelligence 44, no. 7 (2021): 3523-3542.
17. Haskins, Grant, Uwe Kruger, and Pingkun Yan. "Deep learning in medical image registration: a survey." Machine Vision and Applications 31 (2020): 1-18.
18. Xu, Mai, Chen Li, Shanyi Zhang, and Patrick Le Callet. "State-of-the-art in 360 video/image processing: Perception, assessment and compression." IEEE Journal of Selected Topics in Signal Processing 14, no. 1 (2020): 5-26.
19. Han, X., J. Chang, and K. Wang. "You only look once: unified, real-time object detection." Procedia Computer Science 183, no. 1 (2021): 61-72.
20. Masurekar, Omkar, Omkar Jadhav, Prateek Kulkarni, and Shubham Patil. "Real time object detection using YOLOv3." International Research Journal of Engineering and Technology (IRJET) 7, no. 03 (2020): 3764-3768.
21. Dhillon, Anamika, and Gyanendra K. Verma. "Convolutional neural network: a review of models, methodologies and applications to object detection." Progress in Artificial Intelligence 9, no. 2 (2020): 85-112.
22. Tarnowski, Paweł, Marcin Kołodziej, Andrzej Majkowski, and Remigiusz J. Rak. "Emotion recognition using facial expressions." Procedia Computer Science 108 (2017): 1175-1184.