Volume 14 , Issue 2 , PP: 343-351, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Faqeda Hassen Kareem 1 * , Mohammed Abdullah Naser 2
Doi: https://doi.org/10.54216/JCIM.140225
Face detection is a crucial aspect of computer vision and image processing, in order to enable the automatic detection and identification of human faces in video streams, face detection is an essential component of computer vision and image processing. Applications for facial recognition, video analytics, security systems, and surveillance all depend on it. Face identification techniques face many obstacles and issues, such as positional fluctuations, illumination changes, resolution and scale issues, facial emotions, and cosmetics. Robust algorithms are required for efficient face detection. This field looks at the feature extraction process using a variety of techniques. These consist of the center symmetric local binary patterns (CS_LBP) approach and the local binary patterns (LBP) method. The YouTube Face database provided the video frames that we used for our study. In order to train the convolutional neural network (CNN) to detect human faces in the video and draw a bounding box around them. The experimental results of the suggested approaches show that. The accuracy rate was 94% higher with the LBP techniques. However, the CS_LBP technique showed the best level of accuracy in both face detection and face rectangle recognition, with an accuracy rate of 95%.
Face Detection , Spatial Feature Extraction , CNN , LBP , CS_LBP
[1] D. Mamieva, A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Improved face detection method via learning small faces on hard images based on a deep learning approach,” Sensors, vol. 23, no. 1, p. 502, 2023.
[2] N. Zhang, J. Luo, and W. Gao, “Research on face detection technology based on MTCNN,” Proc. - 2020 Int. Conf. Comput. Network, Electron. Autom. ICCNEA 2020, pp. 154–158, 2020, doi: 10.1109/ICCNEA50255.2020.00040.
[3] J. Deng, J. Guo, X. An, Z. Zhu, and S. Zafeiriou, “Masked Face Recognition Challenge: The InsightFace Track Report,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2021-Octob, pp. 1437–1444, 2021, doi: 10.1109/ICCVW54120.2021.00165.
[4] Aziz, A. Mirzaliev, S. "Ransomware Threats in Industrial Internet of Things Networks: A Detection Approach," Journal of International Journal of Wireless and Ad Hoc Communication, vol. 8, no. 1, pp. 15-20, 2024. DOI: https://doi.org/10.54216/IJWAC.080102
[5] S. Yang, P. Luo, C.-C. Loy, and X. Tang, “Wider face: A face detection benchmark,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5525–5533.
[6] L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. big Data, vol. 8, pp. 1–74, 2021.
[7] J. Dai, “NIPS-2016-r-fcn-object-detection-via-region-based-fully-convolutional-networks-Paper.pdf,” no. Nips, 2016.
[8] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst., vol. 28, 2015.
[9] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
[10] W. Liu et al., “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, Springer, 2016, pp. 21–37.
[11] Vimala, S. Krishnan, V. Raj, M. Kumar, A. Janakiraman, M. "Object Detection Using Deep Learning," Journal of Journal of Cognitive Human-Computer Interaction, vol. 6, no. 1, pp. 32-38, 2023. DOI: https://doi.org/10.54216/JCHCI.060103
[12] A. S. Joshi, S. S. Joshi, G. Kanahasabai, R. Kapil, and S. Gupta, “Deep Learning Framework to Detect Face Masks from Video Footage,” Proc. - 2020 12th Int. Conf. Comput. Intell. Commun. Networks, CICN 2020, pp. 435–440, 2020, doi: 10.1109/CICN49253.2020.9242625.
[13] W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: a real-time face detector,” Vis. Comput., vol. 37, no. 4, pp. 805–813, 2021, doi: 10.1007/s00371-020-01831-7.
[14] N. Kulkarni, D. Mantri, P. Pawar, M. Deshmukh, and N. Prasad, “Occlusion and Spoof Attack Detection using Haar Cascade Classifier and Local Binary Pattern for Human Face Detection for ATM,” AIP Conf. Proc., vol. 2494, no. October, 2022, doi: 10.1063/5.0107262.
[15] Y. Liu, R. Liu, S. Wang, D. Yan, B. Peng, and T. Zhang, “Video Face Detection Based on Improved SSD Model and Target Tracking Algorithm,” J. Web Eng., vol. 21, no. 2, pp. 545–567, 2021, doi: 10.13052/jwe1540-9589.21218.
[16] E. Solomon, A. Woubie, and E. S. Emiru, “Autoencoder Based Face Verification System,” 2023, [Online]. Available: http://arxiv.org/abs/2312.14301
[17] L. Wolf, T. Hassner, and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 529–534, 2011, doi: 10.1109/CVPR.2011.5995566.
[18] W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,” J. Phys. Conf. Ser., vol. 1591, no. 1, 2020, doi: 10.1088/1742-6596/1591/1/012028.
[19] B. T. Devi and S. Shitharth, “Multiple Face Detection Using Haar - AdaBoosting, LBP-AdaBoosting and Neural Networks,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1042, no. 1, p. 012017, 2021, doi: 10.1088/1757-899x/1042/1/012017.
[20] A. B. Shetty, Bhoomika, Deeksha, J. Rebeiro, and Ramyashree, “Facial recognition using Haar cascade and LBP classifiers,” Glob. Transitions Proc., vol. 2, no. 2, pp. 330–335, 2021, doi: 10.1016/j.gltp.2021.08.044.
[21] B. Yang and S. Chen, “A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image,” Neurocomputing, vol. 120, pp. 365–379, 2013, doi: 10.1016/j.neucom.2012.10.032.
[22] K. Meena and A. Suruliandi, “Local binary patterns and its variants for face recognition,” Int. Conf. Recent Trends Inf. Technol. ICRTIT 2011, no. October, pp. 782–786, 2011, doi: 10.1109/ICRTIT.2011.5972286.
[23] A. D. Salman, M. A. Talab, and R. R. Al‐Dahhan, “Features extraction for robust face recognition using GLCM and CS-LBP,” in Proceedings of International Conference on Emerging Technologies and Intelligent Systems: ICETIS 2021 Volume 2, Springer, 2022, pp. 175–191.
[24] Z. Xiong, M. Liu, and Q. Guo, “Finger Vein Recognition Method Based on Center-Symmetric Local Binary Pattern,” Proc. 2019 IEEE Int. Conf. Artif. Intell. Comput. Appl. ICAICA 2019, pp. 262–266, 2019, doi: 10.1109/ICAICA.2019.8873475.
[25] Abd, B. Obaidi, M. "Enhancing Healthcare Data Classification: Leveraging Machine Learning on ChatGPT-Generated Datasets," Journal of International Journal of Advances in Applied Computational Intelligence, vol. 5, no. 2, pp. 34-45, 2024. DOI: https://doi.org/10.54216/IJAACI.050203
[26] V. H. Phung and E. J. Rhee, “A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets,” Appl. Sci., vol. 9, no. 21, p. 4500, 2019.
[27] D. Bhatt et al., “CNN variants for computer vision: History, architecture, application, challenges and future scope,” Electronics, vol. 10, no. 20, p. 2470, 2021
