Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(202.87KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Comparative Analysis of Partial Occlusion Using Face Recognition Techniques
Nallammal.N, V.Radha
Pages - 132 - 139     |    Revised - 05-04-2013     |    Published - 30-04-2013
Volume - 7   Issue - 2    |    Publication Date - April 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Partial Face Occlusion, Non-negative Matrix Factorization (NMF), Local NMF, Spatially Confined NMF.
ABSTRACT
This paper presents a comparison of partial occlusion using face recognition techniques that gives in which technique produce better result for total success rate. The partial occlusion of face recognition is especially useful for people where part of their face is scarred and defect thus need to be covered. Hence, either top part/eye region or bottom part of face will be recognized respectively. The partial face information are tested with Principle Component Analysis (PCA), Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF). The comparative results show that the recognition rate of 95.17% with r = 80 by using SFNMF for bottom face region. On the other hand, eye region achieves 95.12% with r = 10 by using LNMF.
CITED BY (1)  
1 Monteiro, J. C., & Cardoso, J. S. (2015). A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios. Sensors, 15(1), 1903-1924.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 C.C.Teo, H.F.Neo, Andrew B.J.Teoh, “A Study on Partial Face Recognition of Eye Region”.International Conference on Machine Vision, pp. 46-49, 28-29 December 2007, Islamabad,Pakistan.
2 Hamdan, Amani, "The Issue of Hijab in France: Reflections and Analysis," Muslim World Journal of Human Rights: vol. 4 : issue. 2,article 4, 2007.
3 Retrieved from http://www.thesun.co.uk/sol/homepage/news/article76421.ece, 2006.
4 Retrieved from http://www.vfs-uk-my.com/biometrics.aspx, 2005.
5 M. Savvides, R. Abiantun, J. Heo, S. Park, C. Xie and B.V.K. Vijayakumar, “Partial & Holistic Face Recognition On FRGC-II Data Using Support Vector Machine Kernel Correlation Feature Analysis,” Proc. Computer Vision and Pattern Recognition Workshop, 2006.
6 S. Gutta, V. Philomin and M. Trajkovic, “An Investigation Into The Use Of Partial-Faces For Face Recognition,” Proc. 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002.
7 S. Gutta, H. Wechsler, “Partial Faces For Face Recognition: Left Vs Right Half,” 10th International Conference on Computer Analysis of Images and Patterns, LNCS 2756, pp. 630– 637, 2003.
8 K.Hotta, “A Robust Object Tracking Method Under Pose Variation and Partial occlusion”.IEICE Trans. Inf. & Syst., vol. E89-D, no. 7,pp. 2132 – 2141..
9 F. Tarres, A. Rama, “A Novel Method For Face Recognition Under Partial Occlusion Or Facial Expression Variations,” ELMAR 47th International Symposium, pp. 163 – 166, 2005.
10 M. Turk, and A. Pentland, “Eigenfaces for recognition," Journal of Cognitive Neuroscience,vol. 13, no. 1, pp. 71-86, 1991.
11 P.N. Belhumeur, J.P. Hespanha, & D.J. Kriegman, “ Eigenfaces vs fisherfaces: recognition using class specific linear projection,” Proc. Of European Conf. on Computer Vision. 1996.
12 MJ Er, S Wu, J Lu, HL Toh, Face recognition with radial basis function(RBF) neural network.IEEE Transactions on Neural Networks, vol.13, no.3, pp. 697–710 2002.
13 I. Biederman, “Recognition-by-components: a theory of human image understanding.Psychological Review, vol. 94, no. 2, pp. 115-147,1987.
14 Wachsmuth, E., Oram, M.W., & Perrett, D.I., (1994). Recognition of objects and their component parts: responses of single units in the temporal cortex of the macaque. Cereb.Cortex, 22(4):509 - 522.
15 D.D. Lee, and H.S Seung, “Learning the parts of objects by nonnegative matrix factorization,”Nature, vol. 401, pp. 788-791, 1999.
16 S.Z Li,. X.W. Hou, H.J. Zhang and Q. Cheng, “Learning spatially localized, parts-based representation. IEEE CVPR, 2001.
17 H.F. Neo, T.B.J. Andrew and N.C.L. David, “A Novel Spatially Confined Non-Negative Matrix Factorization for Face Recognition,” IAPR Conference on Machine Vision Applications,Tsukuba Science City, Japan. May pp. 16-18, 2005.
18 D.D. Lee, and H.S Seung, “Algorithms for non-negative matrix factorization,” Proceedings of Neural Information Processing Systems, vol. 13, pp. 556-562, 2001.
19 Vision Group of Essex University Face Database,http://cswww.essex.ac.uk/mv/allfaces/index.html, 2004.
20 Han Foon Neo, Chuan Chin Teo, Andrew Beng Jin Teoh, “A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor”, Third International IEEE Conference On Signal-Image Technology & Internet–Based Systems (SITIS 07), pp.735-741, December 16-19, ShangHai, China, 2007.
21 Andrew Teoh Beng Jin, Neo Han Foon, David Ngo Chek Ling. 2005, “Sorted Locally Confined Non-Negative Matrix Factorization in Face,” IEEE International Conference on Communications, Circuits and Systems (ICCCAS'05), Vol. 2, pp. 820-824, 2005.
Mr. Nallammal.N
Avinashilingam Institute for Hmoe science and Higher Education for Women - India
msg2nalls@gmail.com
Dr. V.Radha
Associate Professor /Dept of computer Science Avinashilingam Institute for home science and Higher education for Women Coimbatore, 641 043, Tamil nadu,India - India