Vol. 27 No. 3 (2024) Cover Image
Vol. 27 No. 3 (2024)

Published: September 30, 2024

Pages: 351-356

Articles

Improving Facial Identification System Based on YOLOv8 Deep Learning Algorithm

Huda S. Mithkhal 1 Ahmed H Y Al-Noori 1 Emad Tariq Al-Shiekhly 2
1 Department of Computer Engineering, University of Al-Nahrain, Baghdad-Iraq.
2 College of Business Administration, Prince Mohammad Bin Fahd University, KSA.
History
  • Received: May 5, 2024
  • Revised: July 29, 2024
  • Accepted: August 6, 2024
  • Available Online: September 20, 2024
DOI

https://doi.org/10.29194/NJES.27030351

Abstract

In recent years, the interest in biometric identification systems has increased, due to the worry about security matters in the world.  Face recognition technique is one type of biometric identification technology and is considered the most widely employed .it represents one of the most significant pattern recognition technologies that use characteristics included in facial images or videos to detect the identity of individuals. Recently, it has become more important as it is used in many security applications. In recent years, Most of the traditional facial identification algorithms have faced limitations in identification and verification accuracy. As a result, this work presents a sophisticated system for face identification adopting a novel algorithm of deep learning: You Only Look Once version 8 (YOLOv8). This algorithm can detect the face identity of different individuals with different positions with high accuracy. The face identification technique consists of four main phases which are the image acquisition phase, preprocessing phase, features extraction phase, and classification phase. The YOLOv8 model has been trained for head pose images (HPI) datasets consisting of 1700 images divided as training, validation, and test images of 1190, 255, and 255, respectively. The experimental results show proposed algorithm achieved a significant improvement in face identification accuracy of 99% of mean average precision in which outperforms many state-of-the-art face identification techniques. 

Keywords

References

  1. Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, “Face recognition systems: A survey”, Sensors (Switzerland), vol. 20, no. 2, pp. 905–910, January 2020.
  2. M. Kasar and S. Patil, “Study and Analysis of Facial Landmark Detection Techniques”, Solid State Technology journal, vol.63,no.6,pp.65-77, 2022.
  3. R. Ullah et al., “A Real-Time Framework for Human Face Detection and Recognition in CCTV Images”, Mathematical Problems in Engineering, vol. 43, no.12,pp.61-87, 2022.
  4. A. Elbkosh, “Face Recognition with Symmetrical Face Training Samples Based on Histograms of Oriented Gradients ”, Third Conference for Engineering Sciences and Technology (CEST-2020) 01-03 December 2020 /Alkhoms - Liby, vol.8, no.8 ,December, 2020.
  5. A. A. Aburomman and M. B. I. Reaz, “Ensemble SVM classifiers based on PCA and LDA for IDS”, International Conference on Advances in Electrical, Electronic and Systems Engineering , ICAEES 2016, pp. 95–99, 2016.
  6. F. Deeba and A. Ahmed, “LBPH-based Enhanced Real-Time Face Recognition”, International Journal of Advanced Computer Science and Applications(IJACSA), vol. 10, no. 5, pp. 274–280, 2019.
  7. P. Kamencay, M. Benco, T. Mizdos, and R. Radil, “A new method for face recognition using convolutional neural network”, Advances in Electrical and Electronic Engineering, vol. 15, no. 4 Special Issue, pp. 660–672, 2017.
  8. M. Talib, A. H. Y. Al-Noori, and J. Suad, “YOLOv8-CAB: Improved YOLOv8 for Real-time object detection”, Karbala International Journal of Modern Science, vol. 10, no. 1, 2024.
  9. N. P. Motwani and S. S, ‘Human Activities Detection using DeepLearning Technique- YOLOv8”, ITM Web of Conferences, vol. 56, pp. 03003, 2023.
  10. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”, Computer Science, Computer Vision and Pattern Recognition , vol.76,no.2, Jun. 2015.
  11. Kh.Khan, R. Ullah Khan, J. Ali, I.Uddin, S. Khan,and B. Roh, “Race Classification Using Deep Learning” , Computers, Materials & Continua, vol.68, no. 3, pp. 3484 - 3498, 2016.
  12. Y. Yan, F. Lee, X. Wu, and Q. Chen, “Face recognition algorithm using extended vector quantization histogram features”, PLoS ONE, vol. 13, no. 1, pp. 1–24, 2018.
  13. H. Ling, J. Wu, L. Wu, J. Huang, J. Chen, and P. Li, , “Self residual attention network for deep face recognition”, IEEE Access, vol. 7, pp. 55159–55168, 2019.
  14. ‘S. Karanwal and M. Diwakar, “Feature Fusion of LBP, HELBP & RD-LBP for Face Recognition,” in Distributed Computing and Optimization Techniques, Springer, vol.4, no.2 pp. 32-202, 2022.
  15. S. Gautam et al.,“Screening Autism Spectrum Disorder in childrens using Deep Learning Approach : Evaluating the classification model of YOLOv8 by comparing with other models’ vol.43,no.6,pp. 74-42,2022.
  16. M. T. Abdullah and J. H. Alameri, “A Multi-Weapon Detection Using Synthetic Dataset and Yolov5”, Proceedings - CSCTIT 2022: 5th College of Science International Conference on Recent Trends in Information Technology,vol.4, no.8, pp. 100–104, 2022.
  17. N. Karna, A. P. Putra, S. M. Rachmawati, R. Abiyev, and G. A. Sampedro, “Towards Accurate Fused Deposition Modeling 3D Printer Fault Detection using Improved YOLOv8 with Hyperparameter Optimization”, vol. 23, no.5, pp.432-451,2022.
  18. J. Terven, “A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS”, Machine Learning and Knowledge Extraction, Multidisciplinary Digital Publishing Institute (MDPI), vol. 5 , no. 4 , pp. 1680–1696, Dec. 01, 2023.
  19. N. Gourier, D. Hall, and J. L. Crowley, “Estimating face orientation from robust detection of salient facial structures”, FG Net Workshop on Visual Observation of Deictic Gestures,vol. 6, no.9, pp. 17–25, 2004.
  20. Z.Hao, Q. Feng, and L. Kaidong. “An Optimized Face Detection Based on Adaboost Algorithm”, in 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), vol.13,no.8, pp. 370-378, 2018 .
  21. W. Li , Xi Cen ,Liaojun Pang and Zhicheng Cao “Hyper Face : A Deep Fusion Model for Hyperspectral Face Recognition”. Vol.86, ,no.7,pp.98-110, 2024
  22. S. L. Kuna, “ Real Time Object Detection and Tracking using Deep Learning”, International Journal for Research in Applied Science and Engineering Technology. vol. 8 58–64, 2018.
  23. R. Peng, “Federated learning-based YOLOv8 for face detection”, Applied and Computational Engineering , vol.54 ,No.1, pp. :14-20, march 2024.
  24. S. M. Bah and F. Ming, “An improved face recognition algorithm and its application in attendance management system”, Vol. 5,no.7, pp. 100-124, 2020.
  25. I.Gangopadhyay, A. Chatterjee, and I. Das, “Face detection and expression recognition using HAAR cascade classifier and Fisherface algorithm, in Recent Trends in Signal and Image Processing”. Vol.5, no.8, PP.1-11,2020.