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

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier

by Deepshikha Bhati
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 5
Year of Publication: 2017
Authors: Deepshikha Bhati
10.5120/ijca2017912944

Deepshikha Bhati . Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier. International Journal of Computer Applications. 159, 5 ( Feb 2017), 45-49. DOI=10.5120/ijca2017912944

@article{ 10.5120/ijca2017912944,
author = { Deepshikha Bhati },
title = { Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 5 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number5/27001-2017912944/ },
doi = { 10.5120/ijca2017912944 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:59.941248+05:30
%A Deepshikha Bhati
%T Face Recognition Stationed on DT-CWT and Improved 2DPCA employing SVM Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 5
%P 45-49
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Wavelet Transform is basically used for magnitude depletion. It is used for axing the proportion of picture. Including good multi-resolution and multi-scale analysis, wavelet transform also has the propensity of denoting local signal attribute by using the high and low pass filtering, image can be decomposed into divergent scales of approximation components. But in wavelet transform, the higher decomposition layers will lost a lot of information, by which reduce the recognition rate. 2DPCA is a sort of image extraction method deal directly with image data and does not need dimension reduction. It is undertaking image data without step of vectorization. However 2DPCA algorithm reduces the computational complexity, it takes up more storage space. In that paper proposed an advanced technique which is stationed on dual-tree complex wavelet transform and improved 2DPCA employing SVM classifier in order to give higher coherent recognition rate. The experimental results on ORL and YALE face databases shows that the proposed method improves the performance of face recognition with respect to exiting techniques.

References
  1. Yang Shaohua, Pan Chen, Huang Wenyan. Using Image Preprocessing Method Improved 2DPCA Face Recognition Performance, Journal Of Lanzhou University Of Technology, 2008, 02,34(2),pp. 82-87.
  2. Turk M. Pentland A. Eigen faces For Recognition, Journal of Cognitive Neuroscience, 1991, 3(1), pp. 71-86.
  3. Yang J, Zhang D, Yang J Y. Two- Dimensional PCA: A New Approach To Appearance- Based Face Representation And Recognition, IEEE Trans. on Pattern L Machine Intel. L, 2004, 26(1), pp. 131-137.
  4. Zhang Daoqiang, Zhou Zhihua. Two- Directional Two- Dimensional PCA for efficient Face Representation and Recognition, Neurocomputing , 2005, 69(3), pp.224-231.
  5. Yang Wankou, Ji Shanbing, Ren Mingwu. Based on The Enhanced 2 Dimension Feature Extraction Methods of Principal Component Analysis and its Application in Face Recognition, Chinese Journal of Image, 2009(2), pp. 227-232.
  6. I. W. Selesnick, R.G. Baraniuk, and N. G. Kingsburg, The dual- tree complex wavelet transform- a coherent framework for multiscale signal and image processing, IEEE Signal Process. Mag. , vol. 22, no. 6, pp. 123-151, Nov. 2005.
  7. G. Y. Zhang, S. Y. Peng, and H. M. Li, Combination of dual-tree complex wavelet and svm for face recognition, in Proc. Ibt Conf. Machine Learning and Cybernetics, 2008, vol 5, pp. 2815-2819.
  8. Y. H. Sun and M. H. Du, Dt-CWT feature combined with onpp for face recognition, in computational Intelligence and Security. Berlin/ Heidelberg, Germany: Springer, 2007, vol. 4456, pp. 1058-1067.
  9. Z. M. Huang, X. H. Zhang, and D. Yang. Face recognition based on dual-tree complex wavelet feature, J. Comput. Appl. , vol. 27, no. 5, pp. 1135-1137, 2007.
  10. Wang YuDe Zhao HuanLi, Xue NaiYu. Face feature Extraction And Recognition Algorithm Based On the Block OF The Wavelet Transform And 2DPCA, Infrared And Laser Engineering 2012. Pp.3119-3121.
  11. N. G. Kingsbury, Complex wavelets for shift invariant analysis and filtering of signals, Appl. Comput. Harmon. Anal. , vol. 10, no. 3, pp. 234-253, 2001.
  12. H. Rabbani and M. Vafadust, Image/video denoising based on a mixture of laplace distributions with local parameters in multiimensional complex wavelet domain, Signal Process. , vol. 88, pp. 158-173, 2008.
  13. E. H. S. Lo, M.R. Pickering, M. R. Frater, and J. F. Arnold. Image segmentation using invarient texture features from the double dyadic dual-tree complex wavelet transform, in Proc. IEEE Int. conf. Acoustics, speech and signal Processing, Apr. 2007, vol. 1, pp. I-609-I-612.
  14. G. Y. Chen, T. D. Bui, and A. Krzyzak,Invarient pattern recognition using radon, dual- tree complex wavelet and fourier transforms , Pattern Recognition. , vol. 42, no. 9, pp. 2013-2019, sep. 2009.
  15. T. Celik, H. Ozkaramanli, and H. Demirel.Facial feature extraction using complex dual- tree wavelet transform, comput. Vis. Image Understand. , vol. 111, pp. 229-246, 2008.
  16. Li Tong. Research. On Human Face Recognition Method Based On The Improved 2DPCA, Journal of Chongqing Industry And Commerce University, 2012. Pp. 46-48.
  17. Cortes.C., Vapnik.V. (1995).Support- Vector Networks, Machine Learning 20(3): 273. Doi:10.1007/BF00994018.
  18. Han XiaoCui. An Improved 2DPCA Face Recognition Method, Computer Engineering And Application, 2010, 46(25). Pp.185-187.
  19. Li WuJun, Wang Chongjun, Zhang Wei, Face Recognition technology, Journal Of Pattern Recognition And Artificail Intelligence, 2006, 19(1). Pp. 58-66.
  20. Wan MingHua, Liu Zhonghua Jin Zhong. A Face Recognition Method Based On The 2DPCA And SVM, Journal Of Communication Technology. 2009, 42(5), pp. 100-102.
  21. ORL database: http://www.camorl.co.uk.
  22. Yale Face Database: http://cvc.yale.edu/projects/yalefaces
  23. M.Pal , D.Bhati, B.kaushik, H.Banka. Solving Classification Problem Using Reduced Dimesion and Eigen Structure in RSVM, International Journal of Computer Applications( 0975-8887) volume 117-no.21, may 2015.
  24. D.Bhati, V.Gupta. Survey- A Comparative Analysis of Face Recognition Technique, International Journal of Engineering Research and Genral Science Volume 3, issue 2, March- April , 2015 ISSN 2091-2730.
  25. Aili Wang, Na Jiang and Yuan Feng. Face Recognition Based on Wavelet Transform and Improved 2DPCA, 2014 Fourth International Confrence on Instrumentation and Measurement, computer, communication and control, 2014.
  26. Dandpat, Swarup Kumar, and Sukadev Meher. Performance improvement for face recognition using PCA and two dimensional PCA, 2013 International confrence on computer communication and informatics, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition PCA 2DPCA Improved 2DPCA DT-CWT SVM.

Powered by PhDFocusTM