Authentication System by Facial Recognition with Principal Component Analysis and Deep Neural Networks

Authors

Keywords:

Authentication, Facial Recognition, Neural Networks, Pattern Recognition, Deep Learning

Abstract

The requirement for a facial recognition framework is reliable in terms of in-plane head lighting and movement conditions; has increased as a result of recent technological advancements and rising business demand. Principal Component Analysis (PCA) and neural networks are both used in the proposed and validated face recognition approach. In the suggested method, significant facial features are extracted, their dimensions are reduced using PCA, and then the facial features are classified using neural networks. The algorithm's outcomes are compiled and contrasted with those of other algorithms that were evaluated on the same dataset, particularly the well-known eigenfaces method and a convolutional neural network approach. We find that the suggested method can train significantly more quickly than the convolutional neural network method and can achieve a higher successful rate than either of the other two methods. The paper includes a discussion and comparison of the findings as well as a procedure walkthrough of the way the research was carried out.

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Published

16.12.2022

How to Cite

Natsheh, E. ., & Said-Ahmed, H. . (2022). Authentication System by Facial Recognition with Principal Component Analysis and Deep Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 179–183. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2213

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Section

Research Article