Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Integer Wavelet-based PCA for Face Recognition

Print
PDF
International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 54 - Number 7
Year of Publication: 2012
Authors:
Siba Shankar Rout
10.5120/8579-2323

Siba Shankar Rout. Article: Integer Wavelet-based PCA for Face Recognition. International Journal of Computer Applications 54(7):25-29, September 2012. Full text available. BibTeX

@article{key:article,
	author = {Siba Shankar Rout},
	title = {Article: Integer Wavelet-based PCA for Face Recognition},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {54},
	number = {7},
	pages = {25-29},
	month = {September},
	note = {Full text available}
}

Abstract

Face recognition has received significant attention from re-searchers in biometrics, pattern recognition and computer vision Communities. Fixed point implementation has limitless advantages than Floating point implementation, i. e. suitable for hardware design, low computational complexity, high speed, less memory required to store, low power consumption & easy in encoding. Hence fixed point implementation (Integer Wavelet Transform) is best candidate than floating point implementation (Classical Wavelet Transform). There are many different integer wavelet filters which can be used in the transformation stage and the choice of the filter would have some influence on the accuracy rate of the Face Recognition. This paper proposes a PCA on Integer Wavelet domain for face retrieval system which requires less memory as well with less computational complexity than the traditional methods like PCA and Fisher approaches. Aiming at the LL band as feature of image, a feature extraction and image retrieval algorithm using various Integer Wavelet Transform (IWT) is proposed. Since LL subband of wavelet decomposition becomes the input for PCA hence the memory usage can be greatly reduced. All tests and experiments are carried out by using MATLAB as computing environment and programming language. Experimental result shows that the proposed recognition system with very good performance nearly 98% as recognition accuracy.

References

  • M. Turk, A. Pentland. Eigen faces for Recognition. Journal of Cognitive Science , 3(1):7186, 1991.
  • Jon Shlens,"A Tutorial on Principal Component Analysis Derivation, Discussion and Singular Value Decomposition", 25 March 2003, Version 1.
  • JEkenel, H. K. and Sanker, B. (2005). Multiresolution face recognition, Image and Vision Computing, Vol. 23, (May 2005) pp. 469-477.
  • A. R. Calderbank, I. Daubechies, W. Sweldens, and B. L. Yeo, "Wavelet transforms that Map Integers to Integers", it Applied Computational and Harmonic Analysis, VOL. 5, NO. 3, pp 332-369, 1998.
  • Jian Huang Lai, Pong C. Yuen and Guo Can Feng, "Face recognition using holistic Fourier invariant features", The Journal of the Pattern Recognition Society, pp 95-109, 2001.
  • Hussein Rady, "Face Recognition using Principle Component Analysis with Different Distance Classifiers", IJCSNS International Journal of Computer Science and Network Security, VOL. 11 No. 10, (October 2011) pp. 134-144.
  • AT and T Laboratories, Cambridge, U. K. , The ORL Face Database, Available at: http://www. orl. co. uk/facedatabase. html.
  • Y P. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The feret database and evaluation procedure for face-recognition algorithms", vol. 16, pp. 295306,April 1998.
  • The Japanese Female Facial Expression (JAFFE) Database,Available at : http://www. kasrl. org/jaffe. htmlIn Distributed Systems, S. Mullender