CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Framework for Reduction of Principal Component Analysis based Algorithms using Residue Number System

by Tajudeen N. Madandola, Kazeem A. Gbolagade, Yusuf-Asaju Ayisat W.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 31
Year of Publication: 2019
Authors: Tajudeen N. Madandola, Kazeem A. Gbolagade, Yusuf-Asaju Ayisat W.
10.5120/ijca2019919192

Tajudeen N. Madandola, Kazeem A. Gbolagade, Yusuf-Asaju Ayisat W. . A Framework for Reduction of Principal Component Analysis based Algorithms using Residue Number System. International Journal of Computer Applications. 178, 31 ( Jul 2019), 41-44. DOI=10.5120/ijca2019919192

@article{ 10.5120/ijca2019919192,
author = { Tajudeen N. Madandola, Kazeem A. Gbolagade, Yusuf-Asaju Ayisat W. },
title = { A Framework for Reduction of Principal Component Analysis based Algorithms using Residue Number System },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2019 },
volume = { 178 },
number = { 31 },
month = { Jul },
year = { 2019 },
issn = { 0975-8887 },
pages = { 41-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number31/30738-2019919192/ },
doi = { 10.5120/ijca2019919192 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:51:57.781204+05:30
%A Tajudeen N. Madandola
%A Kazeem A. Gbolagade
%A Yusuf-Asaju Ayisat W.
%T A Framework for Reduction of Principal Component Analysis based Algorithms using Residue Number System
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 31
%P 41-44
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The level of criminality in Nigeria is at alarming rate which leads to daily development of various security skills used in identification and verification of offenders. Face Recognition (FR) which is one of the security skills is a biometric system used to identify or verify a person from a digital image. The dimensionality reduction is a most essential duty in the domain of FR. An algorithm that is used most of the times in FR for dimensionality reduction is Principal Component Analysis (PCA). PCA is a technique that can be used for dimensionality reduction but the major challenge of PCA is that it is a time-consuming data mining algorithm. Reducing the execution time of PCA has been a research significant topic in pattern recognition and computer vision. This paper presents a framework for execution time reduction of Principal Component Analysis based Algorithms focusing on CRT form of Residue Number System. Yale database and another new dataset is created containing 120 face images of 40 persons frontal faces with 3 images of each individual for the experiment.The proposed approach would decrease the execution time of PCA algorithm base algorithms.

References
  1. Bankas, E. K., and Gbolagade, K. A. 2014. A new efficient RNS reverse converter for the 4-Moduli Set {2n, 2n+ 1, 2n − 1, 22n+1– 1}. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 8(2): 328 - 332.
  2. Barati, A., Movaghar, A., and Sabaei, M. 2014. Energy efficient and high-speed error control scheme for real time wireless sensor networks. International Journal of Distributed Sensor Networks. 1-9.
  3. Draper, K.B., Bartlett, M.S. and Beveridge, J. R. 2002. “Recognizing Faces with PCA and ICA,” Computer Vision and Image Understanding: special issue on face recognition, in press.
  4. Gbolagade, K. A. 2010. Effective Reverse Conversion in Residue Number System Processors. PhD Thesis. The Netherlands.
  5. Goldstein, A.J., Harmon, L. D., and Lesk, A. B. 2002. “Identification of Human Faces,” in Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. 59: 748 – 760.
  6. Jolliffe I.T and Cadima J. 2016. Principal component analysis: A review and recent developments. Phil. Trans. R. Soc. A 374: 20150202.
  7. Kirby, M. and Sirovich, L. 1990. “Application of the KL Procedure for the Characterization of Human Faces,” IEEE Trans. Pattern Analysis and Machine Intelligence. 12(1): 103-108.
  8. Li, Y., Fermuller, C., Aloimonos, Y. and Hui, J. 2010. “Learning shift-invariant sparse representation of actions,” in Proc. IEEE Conf. CVPR: 2630–2637.
  9. Penev, P.S. and Sirovich, L. 2000, “The Global Dimensionality of Face Space,” Proc. Fourth IEEE Int’l Conf. Automatic Face and Gesture Recognition. 264- 270.
  10. Sirovich, L. and Kirby, M. 1988. “Low-Dimensional Procedure for Characterization of Human Faces,” J. Optical Soc. Am. 4: 519-524.
  11. Stan Z. Li and Anil K. Jain. 1999. “Handbook of Face Recognition” Springer publications.
  12. Szabo, N. and Tanaka, R. 1967. Residue Arithmetic and its Application to Computer Technology. MC-Graw-Hill, New York.
  13. Turk, M. A. and Pentland, A. 1991a. “Face Recognition Using Eigenfaces,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition. 586–591.
  14. Turk, M.A. and Pentland, A. 1991b. " Eigenfaces for Recognition." vision and Modeling Group , The Media Laboratory, Massachusetts Institute of Technology.
  15. Turk, M.A. and Pentland, A. 1991c. “Eigenfaces for Recognition,” J. Cognitive Neuroscience. 3(1): 71-86.
  16. Valentin, D., Abdi, H., O’Toole, A.J. and Cottrell, G.W. 1994. “Connectionist Models of Face Processing: a Survey,” Pattern Recognition. 27(9): 1209-1230.
  17. Wiskott, L., Fellous, J.M., Kru¨ger, N. and Malsburg, C. von der. 1997. “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. Pattern Analysis and Machine Intelligence. 19(7): 775-779.
  18. Zhao, L. and Yang, Y. 1999. “Theoretical Analysis of Illumination in PCA-Based Vision Systems,” Pattern Recognition. 32(4): 547-564.
  19. Zhao, Q., Zhou, G., Adali, T. Zhang, L. and Cichocki, A. 2013. “Kernelization of tensor-based models for multiway data analysis,” IEEE Signal Process. Mag.. 30(4): 137–148.
  20. Zhihui, Lai, Yong Xu, Qingcai Chen, Jian Yang. and David Zhang. 2014. Multilinear Sparse Principal Component Analysis. IEEE Transactions on Neural Networks and Learning System.
  21. The Yale database, Available: http://cvc.yale.edu/
  22. The pattern recognition Generic images, Available http.www.face-rec.org
Index Terms

Computer Science
Information Sciences

Keywords

Residue Number System Eigenfaces Euclidean distance principal component analysis dimensionality reduction

Powered by PhDFocusTM