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Hybrid Machine Learning Model for Face Recognition Using SVM

Anil Kumar Yadav1, R. K. Pateriya2, Nirmal Kumar Gupta3, Punit Gupta4,*, Dinesh Kumar Saini4, Mohammad Alahmadi5

1 IES College of Technology, Bhopal, 462044, India
2 Maulana Azad National Institute of Technology, Bhopal, 462003, India
3 Department of Information Technology, Manipal University Jaipur, Jaipur, 303007, India
4 Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, 303007, India
5 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23218, Saudi Arabia

* Corresponding Author: Punit Gupta. Email: email

Computers, Materials & Continua 2022, 72(2), 2697-2712. https://doi.org/10.32604/cmc.2022.023052

Abstract

Face recognition systems have enhanced human-computer interactions in the last ten years. However, the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations. Principal Component Analysis-Support Vector Machine (PCA-SVM) and Principal Component Analysis-Artificial Neural Network (PCA-ANN) are among the relatively recent and powerful face analysis techniques. Compared to PCA-ANN, PCA-SVM has demonstrated generalization capabilities in many tasks, including the ability to recognize objects with small or large data samples. Apart from requiring a minimal number of parameters in face detection, PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN. PCA-SVM, however, is ineffective and inefficient in detecting human faces in cases in which there is poor lighting, long hair, or items covering the subject's face. This study proposes a novel PCA-SVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection. The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

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APA Style
Yadav, A.K., Pateriya, R.K., Gupta, N.K., Gupta, P., Saini, D.K. et al. (2022). Hybrid machine learning model for face recognition using SVM. Computers, Materials & Continua, 72(2), 2697-2712. https://doi.org/10.32604/cmc.2022.023052
Vancouver Style
Yadav AK, Pateriya RK, Gupta NK, Gupta P, Saini DK, Alahmadi M. Hybrid machine learning model for face recognition using SVM. Comput Mater Contin. 2022;72(2):2697-2712 https://doi.org/10.32604/cmc.2022.023052
IEEE Style
A.K. Yadav, R.K. Pateriya, N.K. Gupta, P. Gupta, D.K. Saini, and M. Alahmadi "Hybrid Machine Learning Model for Face Recognition Using SVM," Comput. Mater. Contin., vol. 72, no. 2, pp. 2697-2712. 2022. https://doi.org/10.32604/cmc.2022.023052



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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