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Face Recognition using Multilevel Block Truncation Coding

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International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 36 - Number 11
Year of Publication: 2011
Authors:
Dr. H. B. Kekre
Dr. Sudeep D. Thepade
Sanchit Khandelwal
Karan Dhamejani
Adnan Azmi
10.5120/4536-6456

Dr. H B Kekre, Dr. Sudeep D Thepade, Sanchit Khandelwal, Karan Dhamejani and Adnan Azmi. Article: Face Recognition using Multilevel Block Truncation Coding. International Journal of Computer Applications 36(11):38-44, December 2011. Full text available. BibTeX

@article{key:article,
	author = {Dr. H. B. Kekre and Dr. Sudeep D. Thepade and Sanchit Khandelwal and Karan Dhamejani and Adnan Azmi},
	title = {Article: Face Recognition using Multilevel Block Truncation Coding},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {36},
	number = {11},
	pages = {38-44},
	month = {December},
	note = {Full text available}
}

Abstract

Face Recognition is one of the fastest growing biometric technologies to be used in real time applications as it requires lesser user co-operation when compared to other biometrics like fingerprint and iris recognition. Such applications require a very less recognition time and allow for a little leeway on the accuracy front; this is achieved by finding out the feature vector of a face image. The paper presents use of Multilevel Block Truncation coding for face recognition. In all four levels of Multilevel Block Truncation Coding are considered for feature vector extraction resulting into four variations of proposed face recognition technique. The experimentation has been conducted on two different face databases. The first one is Face Database which has 1000 face images and the second one is “Our Own Database” which has 1600 face images. To measure the performance of the algorithm the False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) parameters have been used. The experimental results have shown that the outcome of BTC Level 4 is better as compared to the other BTC levels in terms of accuracy, at the cost of increased feature vector size.

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