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
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: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
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).