Generic placeholder image

Recent Advances in Computer Science and Communications

Author(s): Ratnesh Kumar Shukla*, Arvind Kumar Tiwari and Ashish Ranjan Mishra

DOI: 10.2174/0126662558282684240213062932

DownloadDownload PDF Flyer Cite As
Face Recognition Using LBPH and CNN

Article ID: e150324228026 Pages: 11

  • * (Excluding Mailing and Handling)

Abstract

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities.

Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms.

Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN.

Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

[1]
C. Jagadeeswari, and M.U. Theja, "Performance evaluation of] intelligent face mask detection system with various deep learning] classifiers", Int. J. Adv. Sci. Technol., vol. 29, no. 11, pp. 3083-3087, 2020.
[2]
W. Hariri, "Efficient masked face recognition method during the covid-19 pandemic", The arXiv preprint 2105.03026.
[3]
A.K. Tiwari, and R.K. Shukla, "Machine learning approaches for face identification feed forward algorithms", In Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE), 2019Sultanpur, Uttar Pradesh, India
[http://dx.doi.org/10.2139/ssrn.3350264]
[4]
K. Teke, A. Manjare, and S. Jamdar, "Survey on face mask detection using deep learning", Int. J. Data Sci. Mach. Learn. Appl., vol. 1, no. 1, pp. 1-9, 2021.
[5]
Z. Wang, G. Wang, B. Huang, Z. Xiong, and W.H. Hong, "Masked face recognition dataset and application", The arXiv preprint 2003.09093.
[6]
N. Fasfous, M.R. Vemparala, A. Frickenstein, L. Frickenstein, and M. Badawy, "BinaryCoP: Binary neural network-based COVID-19 face-mask wear and positioning predictor on edge devices", In IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2021, pp. 108-115
[http://dx.doi.org/10.1109/IPDPSW52791.2021.00024]
[7]
V.P. Singh, and R. Srivastava, "Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map", Biocybern. Biomed. Eng., vol. 38, no. 1, pp. 90-105, 2018.
[8]
J. Tomás, A. Rego, S. Viciano-Tudela, and J. Lloret, "Incorrect facemask-wearing detection using convolutional neural networks with transfer learning", Health care, vol. 9, no. 8, p. 1050, 2021.
[http://dx.doi.org/10.3390/healthcare9081050] [PMID: 34442187]
[9]
A. Alzu’bi, F. Albalas, T. AL-Hadhrami, L.B. Younis, and A. Bashayreh, "“Masked face recognition using deep learning: A review”", Electronics, vol. 10, no. 21, p. 2666, 2021.
[http://dx.doi.org/10.3390/electronics10212666]
[10]
N. Ud Din, K. Javed, S. Bae, and J. Yi, "A novel GAN-based network for unmasking of masked face", IEEE Access, vol. 8, pp. 44276-44287, 2020.
[http://dx.doi.org/10.1109/ACCESS.2020.2977386]
[11]
Jian Yang, D. Zhang, A.F. Frangi, and Jing-yu Yang, "Two-dimensional pca: A new approach to appearance-based face representation and recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 1, pp. 131-137, 2004.
[http://dx.doi.org/10.1109/TPAMI.2004.1261097] [PMID: 15382693]
[12]
R. Huang, V. Pavlovic, and D. Metaxas, "A hybrid face recognition method using markov random fields",
International Conference on Pattern Recognition, 2004pp. 157-160 26 August, Cambridge, UK [http://dx.doi.org/10.1109/ICPR.2004.1334492]
[13]
S. Kukreja, and G. Rekha, "Comparative study of different face recognition techniques", In International Conference on Computational Intelligence and Communication Networks, 2011, pp. 271-273
[http://dx.doi.org/10.1109/CICN.2011.55]
[14]
Y. Ma, and S.B. Li, "The modified eigenface method using two thresholds", Int. J. Comput. Info. Eng., vol. 2, no. 9, pp. 3233-3236, 2008.
[15]
H. Hu, A. Shah, M. Bennamoun, and M. Molton, "2D and 3D face recognition using convolutional neural network",
IEEE Region 10 Conference, 2017pp. 133-132 5−8 November, Penang, Malaysia [http://dx.doi.org/10.1109/TENCON.2017.8227850]
[16]
M. Shalmoly, and B. Soumen, "Face recognition using PCA and minimum distance classifier", In Fifth International Conference on Frontiers in Intelligent Computing: Theory and Applications2016pp. 397-405 Bhubaneswar, India
[17]
N.R. Misra, S. Kumar, and A. Jain, "A review on E-waste: Fostering the need for green electronics", In International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)2021pp. 1032-1036 Greater Noida, India
[http://dx.doi.org/10.1109/ICCCIS51004.2021.9397191]
[18]
S. Kumar, A. Jain, K.A. Agarwal, S. Rani, and A. Ghimire, "Object-based image retrieval using the U-net-based neural network", Comput. Intell. Neurosci., vol. 2021, pp. 1-14, 2021.
[http://dx.doi.org/10.1155/2021/4395646] [PMID: 34804141]
[19]
A. Anwar, and A. Raychowdhury, "Masked face recognition for secure authentication", The arXiv preprint 2008.11104.
[20]
G.J. Chowdary, N.S. Punn, S.K. Sonbhadra, and S Agarwal, "Face mask detection using transfer learning of inceptionv3", arXiv:2009.08369, 2020.
[http://dx.doi.org/10.1007/978-3-030-66665-1_6]
[21]
A. Agarwal, and A. Jain, "Synthesis of 2D and 3D NoC mesh router architecture in HDL environment", JARDCS, vol. 11, no. 4, pp. 2573-2581, 2019.
[22]
B. Qin, and D. Li, "Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID-19", Sensors, vol. 20, no. 18, p. 5236, 2020.
[http://dx.doi.org/10.3390/s20185236] [PMID: 32937867]
[23]
S. Sethi, M. Kathuria, and T. Kaushik, "Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread", J. Biomed. Inform., vol. 120, p. 103848, 2021.
[http://dx.doi.org/10.1016/j.jbi.2021.103848] [PMID: 34171485]
[24]
S.E. Eikenberry, M. Mancuso, E. Iboi, T. Phan, and K. Eikenberry, "To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic", Infect. Dis. Model., vol. 5, pp. 293-308, 2020.
[25]
H.N. Vu, M.H. Nguyen, and C. Pham, "Masked face recognition with convolutional neural networks and local binary patterns", Appl. Intell., vol. 4374, pp. 1-16, 2021. [PMID: 34764616
[26]
Y. Li, K. Guo, Y. Lu, and L. Liu, "Cropping and attention based approach for masked face recognition", Appl. Intell., vol. 51, no. 5, pp. 3012-3025, 2021.
[http://dx.doi.org/10.1007/s10489-020-02100-9] [PMID: 34764581]
[27]
H. Mliki, S. Dammak, and E. Fendri, "An improved multi-scale face detection using convolutional neural network", Signal Image Video Process., vol. 14, no. 7, pp. 1345-1353, 2020.
[http://dx.doi.org/10.1007/s11760-020-01680-w]
[28]
Q. Zheng, P. Zhao, Y. Li, H. Wang, and Y. Yang, "Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification", Neural Comput. Appl., vol. 33, no. 13, pp. 7723-7745, 2021.
[http://dx.doi.org/10.1007/s00521-020-05514-1]
[29]
Q. Zheng, P. Zhao, D. Zhang, and H. Wang, "MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification", Int. J. Intell. Syst., vol. 36, no. 12, pp. 7204-7238, 2021.
[http://dx.doi.org/10.1002/int.22586]
[30]
Q. Zheng, P. Zhao, H. Wang, A. Elhanashi, and S. Saponara, "Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation", IEEE Commun. Lett., vol. 26, no. 6, pp. 1298-1302, 2022.
[http://dx.doi.org/10.1109/LCOMM.2022.3145647]
[31]
Q. Zheng, X. Tian, Z. Yu, N. Jiang, A. Elhanashi, S. Saponara, and R. Yu, "Application of wavelet-packet transform driven deep learning method in PM2.5 concentration prediction: A case study of Qingdao, China", Sustain Cities Soc., vol. 92, p. 104486, 2023.
[http://dx.doi.org/10.1016/j.scs.2023.104486]
[32]
Q. Zheng, X. Tian, Z. Yu, H. Wang, A. Elhanashi, and S. Saponara, "DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization", Eng. Appl. Artif. Intell., vol. 122, p. 106082, 2023.
[http://dx.doi.org/10.1016/j.engappai.2023.106082]
[33]
S. Oh, J. Choi, and J. Kim, "A tutorial on quantum convolutional neural networks (QCNN)", In 2020 International Conference on Information and Communication Technology Convergence (ICTC), 2020, pp. 236-239
[http://dx.doi.org/10.1109/ICTC49870.2020.9289439]
[34]
N.R. Zhou, T.F. Zhang, X.W. Xie, and J.Y. Wu, "Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution", Signal Process. Image Commun., vol. 110, p. 116891, 2023.
[http://dx.doi.org/10.1016/j.image.2022.116891]
[35]
L.H. Gong, J.J. Pei, T.F. Zhang, and N.R. Zhou, "Quantum convolutional neural network based on variational quantum circuits", Opt. Commun., vol. 550, p. 129993, 2024.
[http://dx.doi.org/10.1016/j.optcom.2023.129993]