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Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Journal from gdlhub / 2016-11-17 01:32:09
By : Muhammad Fachrurrozi, Muhammad Mujtahid, Telkomnika
Created : 2015-06-01, with 1 files

Keyword : Iris Recognition, Biometric, Independent Component Analysis, Support Vector Machine, Iris Processing
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1171

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.

Description Alternative :

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.

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