Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT. (27th October 2022)

• Record Type: Journal Article
• Authors: Devi, R.; Sujatha, P.
• Is Part Of: International journal of intelligent enterprise. Volume 10:Number 1(2023)
• Page Start: 73
• Page End: 98
• Publication Date: 2022-10-27
• Subjects: biometric authentication -- gradient pyramid -- support vector machine -- SVM -- discrete wavelet transformation -- DWT -- principal component analysis -- PCA -- iris and fingerprint -- fusion; Organizational learning -- Periodicals; Knowledge management -- Periodicals; Business intelligence -- Periodicals; Business enterprises -- Design -- Periodicals; Organizational effectiveness -- Periodicals; 658.005
• Languages: English
• ISSNs: 1745-3232

Abstract

Authentication and identification is the most challenging task in our daily life. Biometric system provides an automatic identification of an individual using his/her behavioural or physiological traits. In this work, multimodal biometric traits namely fingerprint and iris, have been used. These traits were pre-processed using Wiener filter and applying some morphological operations. The pre-processed biometric traits were segmented and fused using three algorithms namely discrete wavelet transform (DWT), principal component analysis (PCA) and gradient pyramid (GP). The fused biometric traits using GP provides a better result without losing the meaningful information. The feature extraction and classification were carried out using grey scale co-occurrence matrices (GLCM) and support vector machine (SVM). Authentication using fused biometric traits gives accuracy as 83.75, whereas the accuracy using fingerprint 73.75% and iris was 78.48%.