Face Recognition Using PCA Technique
Divesh N. Agrawal1 , Deepak Kapgate2
Section:Research Paper, Product Type: Journal Paper
Volume-2 ,
Issue-10 , Page no. 59-61, Oct-2014
Online published on Nov 02, 2014
Copyright © Divesh N. Agrawal , Deepak Kapgate . 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.
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IEEE Style Citation: Divesh N. Agrawal , Deepak Kapgate, “Face Recognition Using PCA Technique,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.59-61, 2014.
MLA Style Citation: Divesh N. Agrawal , Deepak Kapgate "Face Recognition Using PCA Technique." International Journal of Computer Sciences and Engineering 2.10 (2014): 59-61.
APA Style Citation: Divesh N. Agrawal , Deepak Kapgate, (2014). Face Recognition Using PCA Technique. International Journal of Computer Sciences and Engineering, 2(10), 59-61.
BibTex Style Citation:
@article{Agrawal_2014,
author = {Divesh N. Agrawal , Deepak Kapgate},
title = {Face Recognition Using PCA Technique},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2014},
volume = {2},
Issue = {10},
month = {10},
year = {2014},
issn = {2347-2693},
pages = {59-61},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=287},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=287
TI - Face Recognition Using PCA Technique
T2 - International Journal of Computer Sciences and Engineering
AU - Divesh N. Agrawal , Deepak Kapgate
PY - 2014
DA - 2014/11/02
PB - IJCSE, Indore, INDIA
SP - 59-61
IS - 10
VL - 2
SN - 2347-2693
ER -
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Abstract
This paper provides the information about Face Recognition Technology which gives the much more security in the field of multimedia and information technology. To provide the protection to the data we keep the password but as we know hackers can break the password, for that we keep password as our face. Thus for accessing some network or PC by an unauthorized person is virtually impossible and it helps to protect our data. It also provides the user friendliness in human interaction with computer as there is no such physical touch. In this image is captured and stored into database in compress form. Its benefits show in retrieval and in matching. Like the applications of teleconferencing and video call, face recognition is more efficient. Most of the cameras have this application of face recognition which detects the human face and shows appropriate square box on face. In this paper there is an introductory part of this technology. This shows the generic framework and variants that are frequently use by the face recognizer. Some well known face recognition algorithms, such as PCA, Eigenfaces, will also be explained in this paper.
Key-Words / Index Term
PCA Technique, Data Flow Diagram, Principal Components Analysis (PCA)
References
[1] R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates", IEEE Trans. PAMI, 1993, (15)10:1042-1052
[2] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, Maui, Hawaii, 1991, pp. 586-591
[3] Zhujie and Y.L. Yu, Face Recognition with Eigenfaces, Unknown source, The Hong Kong University of science & Technology, Clear Water Bay, Kowloon, HK, pp. 434-438
[4] E.Lizama, D. Waldoestl and B. Nickolay, “An Eigenfaces-Based Automatic Face Recognition System” 1997 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, New York, NY, 5 vol. 4535, 1997, pp. 174-177
[5] M. Turk and A. Pentland, “Eigenfaces for Recognition”, Journal of Cognitive Neuroscience, March 1991
[6] Reuters News, “Computer Security Threat On Rise – U.S. Survey”, March 7, 1999.
[7] Zhang, J., Yan, Y., and Lades, M., “Face Recognition: Eigenfaces, Elas-tic Matching, and Neural Nets”, Proc. IEEE, vol.85, no.9, pp.1423-1435, 199
[8] Sung, K. and Poggio, T., "Learning a Distribution-Based Face Model for Human Face Detection", Neural Networks for Signal Processing V, pp. 398-406, Cambridge, Mass. 1995.
[9] Pentland, A.P., Moghaddam, B., Starner, T., and Turk, M.A., “View-based and Modular Eigenspaces for Face Recognition”, Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, pp.84-91, Seattle, WA, 1994.
[10] Chellappa, R., Wilson, C.L., and Sirohey, S., “Human and Machinr Recognition of Faces: A Survey”, Proc. IEEE, vol.83, pp.705-741, May 1995.
[11] Lin, S.-H., Kung, S.Y., and Lin, L.-J., “Face Recognition/Detection by Probabilistic Decision-Based Neural Network”, IEEE Trans. Neu-ral Networks, vol. 8, No. 1, pp.114-132, Jan. 1997
[12] Dempster, A.P., Laird, N.M., and Rubin, D.B., “maximum Likelihood from Incomplete Data via the EM Algorithm”, Journal of Royal Statistics Society, B39, pp.1-38, 1976.s
[13] Harley Geiger (2011-12-06). "Facial Recognition and Privacy". Center for Democracy & Technology. Retrieved 2012-01-10.
[14] R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN 978-0-470-51706-2, 2009
[15] Albiol,A., Albiol,A., Oliver,J., Mossi,J.M.(2012). Who is who at different cameras: people re- identification using depth cameras. Computer Vision, IET. Vol 6(5), 378-387.