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IJMLC 2021 Vol.11(2): 115-120 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.2.1023

A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition

Muhammad Shakeel Faridi, Muhammad Azam Zia, Zahid Javed, Imran Mumtaz, and Saqib Ali

Abstract—Feature extracting and training module can be done by using face recognition neural learning techniques. Moreover, these techniques are widely employed to extract features from human images. Some detection systems are capable to scan the full body, iris detection, and finger print detection systems. These systems have deployed for safety and security intension. In this research work, we compare different machine learning algorithms for face recognition. Four supervised face recognition machine-learning classifiers such as Principal Component Analysis (PCA), 1-nearest neighbor (1-NN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are considered. The efficiency of multiple classification systems is also demonstrated and tested in terms of their ability to identify a face correctly. Face Recognition is a technique to identify faces of people whose images are stored in some databases and available in the form of datasets. Extensive experiments conducted on these datasets. The comparative analysis clearly shows that which machine-learning algorithm is the best in terms of accuracy of image detection. Despite the fact, other identification methods are also very effective; face recognition has remained a major focus of research due to its non-meddling nature and being the easy method of personal identification for people. The findings of this work would be useful identification of a suitable machine-learning algorithm in order to achieve better face recognition accuracy.

Index Terms—Feature extraction, face recognition, linear discriminant analysis, 1-nearest neighbor, support vector machine, principal component analysis.

The authors are with Department of Computer Science, University of Agriculture, Faisalabad, Pakistan (Corresponding author: Muhammad Shakeel Faridi; e-mail: shakeelfaridi@gmail.com, zahidjaved_uaf@yahoo.com, mazamzia@uaf.edu.pk, imranmumtaz@uaf.edu.pk, saqib@uf.edu.pk).

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Cite: Muhammad Shakeel Faridi, Muhammad Azam Zia, Zahid Javed, Imran Mumtaz, and Saqib Ali, "A Comparative Analysis Using Different Machine Learning: An Efficient Approach for Measuring Accuracy of Face Recognition," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 115-120, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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