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

R. Devi and P. Sujatha

International Journal of Intelligent Enterprise, 2023, vol. 10, issue 1, 73-98

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. (search for similar items in EconPapers)
Date: 2023
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