Image Compression and Detection Technique Using Principal Component Analysis
DOI:
https://doi.org/10.26438/ijcse/v7i9.1316Keywords:
PCA, Eigen values, Eigen vectors, image compression, Dimension reductionAbstract
This paper mainly presents face recognition system based on principal component analysis. The goal is to implement the system which is able to distinguish a single face from the larger database. In this research work we are compressing the image using the mathematical tool principal component analysis and then recognize the image from the same data set by the model. First we will describe the basic concepts prevailing with principal component analysis. Then we will see that how principal component can be extracted from a given data set. Then we will go for sampling distribution of Eigen values and Eigen vectors. Then followed by model adequacy test, then we perform our task of image detection. The problem arises when we use high dimensionality space. Because in face or in 3d image, we have different eigen values or vectors and it can’t be fixed due to high dimensions as compared to 2d image. Hence, we use Principal Component Analysis (PCA).
References
[1]. L. I. Smith, “A tutorial on principal components analysis,” February 2002. 537
[2]. S. F. Ding, Z. Z. Shi, Y. Liang, and F. X. Jin, “Information feature analysis and improved algorithm of PCA” International Conference on Machine Learning and Cybernetics. vol.3, Aug 2005, pp. 1756–1761, 2005.
[3]. A. A. Alorf, “Comparison of computer-based and optical face recognition paradigms” vol.4, pp 538-545, 2014.
[4]. P. Kamencay, D. Jelovka, and M. Zachariasova, “The impact of segmentation on face recognition using the principal component analysis (PCA)” Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference Proceedings (SPA), Sept 2011, pp. 1–4. 2011
[5]. B. C. Russell, W. T. Freeman, A. A. Efros, J. Sivic, and A. Zisserman, “Using multiple segmentations to discover objects and their extent in image collections” in Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 2, pp. 1605–1614, 2006.
[6]. S. He, J. Ni, L.Wu, H.Wei, and S. Zhao, “Image threshold segmentation method with 2-D histogram based on multi-resolution analysis” in Computer Science Education, 2009. ICCSE. 4th International Conference, pp. 753–757. July 2009
[7]. I. Kim, J. H. Shim, and J. Yang, “Face detection” Face Detection Project, EE368, Stanford University, vol. 28, pp 538, 539, 2003
[8]. R. Gonzalez, R. Woods, and S. Eddins, “Digital Image Processing Using MATLAB”. Gates mark, ISBN: 978-0-9820854-0-0. 538, 540, 543, 2009.
[9]. M. Turk and A. Pentland, “Face recognition using eigenfaces,” in Proceedings of Computer Vision and Pattern Recognition, 1991. pp. 586–591, ISSN: 1063-6919. 538, 1991.
[10]. J. Cognitive Neuroscience, “Eigenfaces for recognition” vol. 3, pp. 71–86, Jan. 1991.
[11]. W. Yang, C. Sun, L. Zhang, and K. Ricanek, “Laplacian bidirectional pca for face recognition,” Neurocomputing, vol. 74, no. 1, pp. 487–493, 2010.
[12]. M. Al-Amin, “Towards face recognition using eigenface,” International Journal of Advanced Computer Science and Applications, vol. 7, pp 539, 544, 2016.
[13]. Q. Fu, “Research and implementation of pca face recognition algorithm based on matlab,” in MATEC Web of Conferences, vol. 22. 539-544. EDP Sciences, 2015.
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