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Reseach Article

A Comparison between PCA and SIFT Algorithms in Face Recognition Accuracy

by Israa Abdulrauof Othman, Sallam Osman Fageeri, Ashraf Osman Ibrahim
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 39
Year of Publication: 2022
Authors: Israa Abdulrauof Othman, Sallam Osman Fageeri, Ashraf Osman Ibrahim
10.5120/ijais2022451934

Israa Abdulrauof Othman, Sallam Osman Fageeri, Ashraf Osman Ibrahim . A Comparison between PCA and SIFT Algorithms in Face Recognition Accuracy. International Journal of Applied Information Systems. 12, 39 ( October 2022), 52-55. DOI=10.5120/ijais2022451934

@article{ 10.5120/ijais2022451934,
author = { Israa Abdulrauof Othman, Sallam Osman Fageeri, Ashraf Osman Ibrahim },
title = { A Comparison between PCA and SIFT Algorithms in Face Recognition Accuracy },
journal = { International Journal of Applied Information Systems },
issue_date = { October 2022 },
volume = { 12 },
number = { 39 },
month = { October },
year = { 2022 },
issn = { 2249-0868 },
pages = { 52-55 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number39/1131-2022451934/ },
doi = { 10.5120/ijais2022451934 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:33.511700+05:30
%A Israa Abdulrauof Othman
%A Sallam Osman Fageeri
%A Ashraf Osman Ibrahim
%T A Comparison between PCA and SIFT Algorithms in Face Recognition Accuracy
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 39
%P 52-55
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The systems that use facial recognition have contributed to the identification of criminals or wanted persons or even to control attendance and departure. However, the problem is whether the error rate is large. This may cause many problems. There are many useful aid such as SIFT and PCA. The purpose of this research is to analyze and compare the performance i.e. accuracy of principles of Comparison Analysis (PCA) and (SIFT) algorithms. The evaluation method used is a confusion matrix for measuring accuracy in precision, recall, F-measure, and success rate. Based on the comparative analysis, the (SIFT) algorithm gains the accuracy better by variation of compared to (PCA) algorithm in the implementation for 5 research data sets.

References
  1. Mattew Turk and Alex Pentland," Eigenfaces for Recognition," SPIE Vol.1192 IRCVVIn (i989),22-32
  2. Wu, R.; Yan, S.; Shan, Y.; Dang, Q.; Sun, G. Deep image: scaling up image recognition. arXiv 2015, arXiv:1501.02876.
  3. Torfi, A.; Shirvani, R.; Keneshloo, Y.; Fox, E. Natural language processing advancements by deep learning: A survey. arXiv 2020, arXiv:2003.01200.
  4. M.F. Othman, and T.M.S. Yau, ―Comparison of Different Classification Techniques using WEKA for Breast Cancer‖.F.Ibrahim,N.A.AbuOsman,J.Usman,andN.A. Kadri (Eds.). IFMBE Proceedings 15, 2007, pp. 520-523
  5. Fontana,F.A.,Mäntylä,M.V.,Zanoni,M.,andMarino, A. (2015). Comparing and experimenting machine learning techniques for code smell detection. Empirical Software Engineering.
  6. Amazon SageMaker-DeveloperGuide https://docs.aws.amazon.com/sagemaker/latest/dg/how-pca-works.html .10/11/2018 -3:50 PM
  7. I.H. Witten and E. Frank, Practical Machine Learning Tools anda Techniques, Second Edition, USA: Elsevier Inc. & Morgan Kaufmann Publishers,2005.
  8. Huliman, Analisis Akurasi Algoritma Pohon Keputusandan k-Nearest Neighbor (k-NN), Tesis, Universitas Sumatera Utara, 2013.
  9. M.H. Dunham, DATA MINING Introductory and Advanced Topics, USA: Prentice Hall, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Principles of Comparison Analysis PCA SIFT ConfusionMatrix