Performance analysis of iris biometric system using GKPCA and SVM

M. Suganthy and S. Manjula

International Journal of Information Technology and Management, 2021, vol. 20, issue 1/2, 207-216

Abstract: Among all biometric technologies, iris recognition is the most accurate and high confidence authentication system. Due to the limitations in PCA-based system, modified principal component analysis (PCA)-based feature extraction is proposed in iris recognition system. In the proposed system, features are extracted using Gaussian kernel PCA (GKPCA) and classified using support vector machine (SVM). GKPCA and SVM algorithms are evaluated using CASIA V3 iris database. The performances are compared with the existing PCA-based system. The proposed system achieves 96.67% of accuracy for 256 features using GKPCA linear SVM. False acceptance rate (FAR) and false rejection rate (FRR) are 0 and 3 respectively for linear SVM. The results show that the proposed system performs accurate localisation of patterns even in non-ideal conditions.

Keywords: Gaussian kernel principal component analysis; GKPCA; support vector machine; SVM; iris recognition; false acceptance rate; FAR. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:20:y:2021:i:1/2:p:207-216

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