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Review
. 2025 Feb 13;11(2):58.
doi: 10.3390/jimaging11020058.

Facial Recognition Algorithms: A Systematic Literature Review

Nazar El Fadel  1
Affiliations
Review

Facial Recognition Algorithms: A Systematic Literature Review

Nazar El Fadel. J Imaging. .

Abstract

This systematic literature review aims to understand new developments and challenges in facial recognition technology. This will provide an understanding of the system principles, performance metrics, and applications of facial recognition technology in various fields such as health, society, and security from various academic publications, conferences, and industry news. A comprehensive approach was adopted in the literature review of various facial recognition technologies. It emphasizes the most important techniques in algorithm development, examines performance metrics, and explores their applications in various fields. The review mainly emphasizes the recent development in deep learning techniques, especially CNNs, which greatly improved the accuracy and efficiency of facial recognition systems. The findings reveal that there has been a noticeable evolution in facial recognition technology, especially with the current use of deep learning techniques. Nevertheless, it highlights important challenges, including privacy concerns, ethical dilemmas, and biases in the systems. These factors highlight the necessity of using facial recognition technology in an ethical and regulated manner. In conclusion, the paper proposes several future research directions to establish the reliability of facial recognition systems and reduce biases while building user confidence. These considerations are key to responsibly advancing facial recognition technology by ensuring ethical practices and safeguarding privacy.

Keywords: adaptive boosting (ADA); convolutional neural network (CNN); dataset; decision tree (DT); eigenfaces; facial recognition algorithms; fisherfaces; gradient boosting; hyper-parameters; k-nearest neighbor (KNN); naive bayes (NB); performance evaluation; random forest classifier (RFC); support vector machine (SVM); yale facial recognition dataset.

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Conflict of interest statement

The author declares no conflict of interest.

Figures

Figure 1
Figure 1
Summary of related works, approaches, and techniques.
Figure 2
Figure 2
Pilot search results.
Figure 3
Figure 3
Exclusion articles.
Figure 4
Figure 4
Protocol depicted.
Figure 5
Figure 5
Study relevance to RQs.
See this image and copyright information in PMC

References

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    1. Wayman J., Jain A., Maltoni D., Maio D. Biometric Recognition: Principles and Practice. Springer; Berlin/Heidelberg, Germany: 2005.

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