Title: Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT

Authors: R. Devi; P. Sujatha

Addresses: Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai, India ' Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai, India

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%.

Keywords: biometric authentication; gradient pyramid; support vector machine; SVM; discrete wavelet transformation; DWT; principal component analysis; PCA; iris and fingerprint; fusion.

DOI: 10.1504/IJIE.2023.127237

International Journal of Intelligent Enterprise, 2023 Vol.10 No.1, pp.73 - 98

Received: 07 Feb 2019
Accepted: 17 Feb 2020
Published online: 30 Nov 2022 *