Different distance based PCA+LDA fusion Technique for Face recognition

Dhiren Pandit; Dr. Jayesh Dhodiya

doi:https://doi.org/10.14445/22315373/IJMTT-V9P509

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 9 | Number 1 | Year 2014 | Article Id. IJMTT-V9P509 | DOI : https://doi.org/10.14445/22315373/IJMTT-V9P509

Different distance based PCA+LDA fusion Technique for Face recognition

Dhiren Pandit , Dr. Jayesh Dhodiya

Citation :

Dhiren Pandit , Dr. Jayesh Dhodiya, "Different distance based PCA+LDA fusion Technique for Face recognition," International Journal of Mathematics Trends and Technology (IJMTT), vol. 9, no. 1, pp. 95-102, 2014. Crossref, https://doi.org/10.14445/22315373/IJMTT-V9P509

Abstract

Since last few years, face Recognition has become one of the most challenging task in the pattern recognition field. The Face recognition plays very important role in many applications like video surveillance, retrieval of an identity from a database for criminal investigations and forensic applications. The face is considered as good biometric for many reasons: the acquisition process is nonintrusive and does not require collaboration of the subject to be recognized. The acquisition process of a face from a scene is simpler and cheaper than the acquisition of other biometrics as the iris and the fingerprint. On the other hand, many problems arise, because of the variability of many parameters like face expression, pose, scale, lighting, and other environmental parameters.Face recognition involved in application like problem of recognition of an identity in a scene. A system that automatically recognizes a face in a scene, first detects it and normalize it with respect to the pose, lighting and scale. Then, the system tries to associate the face to one or more faces stored in its database, and gives the set of faces that are considered as nearest to the detected face. This requires more computational resources and very robust algorithms for detection, normalization and recognition. In this paper we have implement different face recognition methods like Principle component analysis, Linear discriminant analysis and Fusion of PCA and LDA for face recognition. And better recognition rate is achieved by implementing different similarity measures between images.

Keywords

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References

[1] Belhumeur P., Hespanha J., Kriegman D., “Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, (July 1997) 711-720, 0162-8828.
[2] Eleyan A. Demirel H. “Face Recognition System based on PCA and Feedforward Neural Networks”, Proceedings of Computational Intelligence and Bioinspired Systems, pp. 935-942, 978-3-540-26208-4, Spain, June 2005, Springer-Verlag, Barcelona.
[3] Hu C., Chang Y., Feris R., Turk M.: Manifold based analysis of facial expression. Computer Vision and Pattern Recognition Workshop (2004)
[4] IIT Kanpur Database,http://viswww.cs.umass.edu/ vidit/IndianFaceDatabase/(7)
[5] Jie Wang, K.N.Plataniotis, A.N. Venetsanopoulos, Selecting discriminant eigenfeaces for face recognition, Pattern Recognition Letters, 26, 2005, 1470-1482.
[6] John J. Weng and Daniel L. Swets, “Face Recognition”, Book Biometrics, page 65-86, editeur springer US, ISBN 978-0-387-28539-9, 1999
[7] Kresimir delac, Mislav Grgic, Sonja Grgic, “independant comparative Study of PCA, ICA and LDA on the FERET data Set”, 2006 wiley periodicals,Inc. vol15,p252-260
[8] Kuldeep Singh Sodhi and Madan Lal, Face Recognition Using PCA, LDA and Various Distance Classifiers,Journal of Global Research in Computer Science, Volume 4, No. 3(2013)
[9] M. Turk, and A. Pentland, Eigenfaces for Face Recognition, Journal of Cognitive Neuroscience, vol. 3 num.1 (1991) 71-86.
[10] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Trans. on PAMI, vol.19 num.7 (1997) 711-720.
[11] Philip Miller and Jamie Lyle, The Effect of Distance Measures on the Recognition Rates of PCA and LDA Based Facial Recognition, Digitial Image Processing Clemson Universtiy
[12] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, John Wiley & Sons, USA (2001).
[13] W. Zhao,R.Chellappa and P.J.Phillips , “Subspace Linear Discriminant Analysis for Face Recognition”, April 1999. Tech. rep. CAR-TR-914, Center for Automation Research, University of Maryland, College Park, MD.
[14] W.Zhao, R.Chellappa, J.Phillips and A.Rosenfeld , “Face recognition in still and video images:A litterature survey”, ACM comput surv 35(2003),399-458
[15] Y.V.Lata,ChandraKiran haradwajTungathurthi, H.RamMohanRao, Dr.AGovardhan, Dr.L.P.Reddy,Facial Recognition using Eigenfaces by PCA, International Journal of Recent Trends in Engineering, Vol.1,No.1,May2009 Face recognition by LDA.