Face Comparison Using PSO Algorithm
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - Issue 2011 by IJCTT Journal | ||
| Volume-3 Issue-2 | ||
| Year of Publication : 2011 | ||
| Authors :Dr.A.V.Senthilkumar, J.Savitha. |
Dr.A.V.Senthilkumar, J.Savitha."Face Comparison Using PSO Algorithm"International Journal of Computer Trends and Technology (IJCTT),V3(2):211-214 Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -It is used specially for the compression of images where tolerable degradation is required. With the wide use of computers and consequently need for large scale storage and transmission of data, efficient ways of storing of data have become necessary. With the growth of technology and entrance into the Digital Age, the world has found itself amid a vast amount of information. Dealing with such enormous information can often present difficulties. Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space. It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.JPEG and JPEG 2000 are two important techniques used for image compression. JPEG image compression standard use DCT (DISCRETE COSINE TRANSFORM). The discrete cosine transform is a fast transform. It is a widely used and robust method for image compression. It has excellent compaction for highly correlated data.DCT has fixed basis images DCT gives good compromise between information packing ability and computational complexity.
References-
[1] J. Kennedy and R. Eberhart, Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, vol. 6, pp. 1942–1948, Piscataway, NJ, December 1995.
[2] J. Kennedy and R. C. Eberhart. A Discrete Binary Version of the Particle Swarm Algorithm.Proc. IEEEInternational Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104-4108, Oct. 1997.
[3] W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399–458, 2003.
[4] Gregory Shakhnarovich and Baback Moghaddam. Face recognition in subspaces. Technical Report TR2004-41, Mitsubishi Electric Research Laboratories, 201 Broadway, Cambridge, Massachusetts 02139, 2004.
[5] A. S. Tolba, A.H. El-Baz, and A.A. El-Harby. Face Recognition: A Literature Review. International Journal ofSignal Processing, vol. 2, no. 2, pp. 88-103. 2006
[6] Rabab M. Ramadan and Rehab F. Abdel Kader. Face Recognition using Particle Swarm Optimization-based selected features. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(2):51-66, 2009.
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