Version 1
: Received: 22 August 2023 / Approved: 22 August 2023 / Online: 22 August 2023 (13:22:53 CEST)
How to cite:
Abusham, E. A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition. Preprints2023, 2023081580. https://doi.org/10.20944/preprints202308.1580.v1
Abusham, E. A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition. Preprints 2023, 2023081580. https://doi.org/10.20944/preprints202308.1580.v1
Abusham, E. A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition. Preprints2023, 2023081580. https://doi.org/10.20944/preprints202308.1580.v1
APA Style
Abusham, E. (2023). A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition. Preprints. https://doi.org/10.20944/preprints202308.1580.v1
Chicago/Turabian Style
Abusham, E. 2023 "A Comparative Study of Machine Learning Classifiers Performance with Feature Extraction for Face Recognition" Preprints. https://doi.org/10.20944/preprints202308.1580.v1
Abstract
It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.
Keywords
Machine Learning; Face Recognition; image classification, Feature Extraction
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
