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Face Recognition using Eigenvector and Principle Component Analysis

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 10
Year of Publication: 2012
Dulal Chakraborty
Sanjit Kumar Saha
Md. Al-amin Bhuiyan

Dulal Chakraborty, Sanjit Kumar Saha and Md. Al-amin Bhuiyan. Article: Face Recognition using Eigenvector and Principle Component Analysis. International Journal of Computer Applications 50(10):42-49, July 2012. Full text available. BibTeX

	author = {Dulal Chakraborty and Sanjit Kumar Saha and Md. Al-amin Bhuiyan},
	title = {Article: Face Recognition using Eigenvector and Principle Component Analysis},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {10},
	pages = {42-49},
	month = {July},
	note = {Full text available}


Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person's face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.


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