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Multimodal Biometrics at Feature Level Fusion using Texture Features
Maya V. Karki, S. Sethu Selvi
Pages - 58 - 73     |    Revised - 15-05-2013     |    Published - 30-06-2013
Volume - 7   Issue - 1    |    Publication Date - June 2013  Table of Contents
Multimodal Biometrics, Feature Level, Curvelet Transform, Template Averaging, PCA Features and SVM Classifier.
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
CITED BY (4)  
1 Angadi, S. A., & Hatture, S. M. (2016). Biometric Person Identification System: A Multimodal Approach Employing Spectral Graph Characteristics of Hand Geometry and Palmprint. International Journal of Intelligent Systems and Applications, 8(3), 48.
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Miss Maya V. Karki
Faculty, MSRIT, Dept. of E&C MSRIT Bangalore-54, INDIA - India
Dr. S. Sethu Selvi
Faculty, MSRIT, Dept. of E&C MSRIT Bangalore-54, INDIA - India