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Performance Evaluation of Face Recognition using PCA and N-PCA

by Ajay Kumar Bansal, Pankaj Chawla
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 8
Year of Publication: 2013
Authors: Ajay Kumar Bansal, Pankaj Chawla
10.5120/13266-0753

Ajay Kumar Bansal, Pankaj Chawla . Performance Evaluation of Face Recognition using PCA and N-PCA. International Journal of Computer Applications. 76, 8 ( August 2013), 14-20. DOI=10.5120/13266-0753

@article{ 10.5120/13266-0753,
author = { Ajay Kumar Bansal, Pankaj Chawla },
title = { Performance Evaluation of Face Recognition using PCA and N-PCA },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 8 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number8/13266-0753/ },
doi = { 10.5120/13266-0753 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:48.681614+05:30
%A Ajay Kumar Bansal
%A Pankaj Chawla
%T Performance Evaluation of Face Recognition using PCA and N-PCA
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 8
%P 14-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition has become a valuable and routine forensic tool used by criminal investigators. It is an important area of computer vision research and has gained significant interest in recent years. Efforts in improving security, such as automatic surveillance and the use of biometrics in identification, are partly responsible for this increased interest. However, several challenges remain in improving the accuracy of face recognition under illumination changes, variations in pose, occlusions, and image resolution. This paper presents performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (N-PCA). The experiments are carried out on the ORL, Indian face database and Georgia Tech face database which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images and it has been found that as the number of training images increases efficiency also increases. The result also shows that N-PCA gives better results than PCA.

References
  1. W. Zhao, R. Chellappa, P. Phillips, A. Rosenfeld, "Face recognition: a literature survey", ACM Computing Surveys vol. 35, pp. 399–458, December 2003.
  2. Daugman J. ,"Face and gesture recognition: Overview", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 675-676,1997.
  3. Samal A. and Iyengar P. A. ," Automatic recognition and analysis of human faces and facial expressions: A survey", Pattern Recognition, Vol. 25, No. 1, pp. 65-77,1992
  4. Valentin D. , Abdi H. , et al,"Connectionist models of face processing: a survey", Pattern Recognition, Vol. 27, No. 9, pp. 1209-1230, 1994.
  5. Yang,M. H. ,Kriegman, D. , and Ahuja,N. ," Detecting faces in images: A survey ", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34-58, 2002. .
  6. Pantic M. and Rothkrantz L. J. M. , "Automatic analysis of facial expressions: the state of the art ", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp. 1424-1445, 2000.
  7. S. K. Singh, Mayank Vatsa, Richa Singh, K. K. Shukla, "A Comprative Study of Various Face Recognition Algorithms", IEEE International Workshop on Computer Architectures for Machine Perception (CAMP), pp. 7803-7970 ,5/03, 2003
  8. D. Beymer, "Face recognition under varying pose", in IEEE Conf. on Comp. Vision and Patt. Recog. , pages 756–761, 1994.
  9. M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
  10. K. J. Karande, S. N. Talbar, "Independent component analysis of edge information for face recognition," Image Processing Journal, vol. 3, no. 3, pp. 120-130, 2009.
  11. H. Zhang, W. Deng, 1. Guo, and 1. Yang, "Locality preserving and global discriminant projection with prior information," Machine Vision and Applications Journal, vol. 21, pp. 577-585, 2010.
  12. J. Li, B. Zhao, and H. Zhang, "Face recognition based on PCA and LDA combination feature extraction," 1st IEEE International Conference on Information Science and Engineering, pp. 1240-1243, 2009.
  13. K. Delac, M. Grgic, and S. Grgic, "Independent comparative study of PCA, ICA, and LDA on the FERET data set," Imaging Systems And Technology Journal, vol. 15, pp. 252-260, 2005.
  14. Abhishek Bansal, Kapil Mehta and Sahil Arora," Face Recognition Using PCA & LDA Algorithms", Second International Conference on Advanced Computing & Communication Technologies IEEE, 978-0-7695-4640-7/12, 2012.
  15. S. Haykin. Neural networks and learning machines. 3rd ed. , Pearson, 2009.
  16. T. Pentland, B. Moghadam, and T. Stamer, "View based and modular eigenspaces for face recognition," IEEE Conference on Computer Vision and Pattern Recognition, pp. 84-91, 1994.
  17. M. Kirby, L. Sirovich, "Application of Karhunen–Loeve procedure for the characterization of human faces", IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 12, pp. 103–108, 1990.
  18. M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Maui, Hawaii, pp. 586-591, 1991.
  19. AT&T Laboratories Cambridge Database of faces. http://www. uk. research. att. com:pub/data/att_faces. zip.
  20. Vidit Jain, Amitabha Mukherjee. The Indian Face Database. www. cs. umass. edu/~vidit/IndianFaceDatabas,2002.
  21. Georgia Tech face database developed at the Center for Signal and Image Processing at Georgia Institute of Technology. http://www. anefian. com/research/face_reco. html.
Index Terms

Computer Science
Information Sciences

Keywords

PCA N-PCA Feature Extraction and Euclidean distance.

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