Title: Sub-band-based feature fusion and hybrid fusion approaches for multimodal biometric identification

Authors: D.V. Rajeshwari Devi; Narasimha Rao Kattamuri

Addresses: Department of ECE, GSSS Institute of Engineering and Technology for Women, Mysuru, 570016, India ' Department of EIE, BMS College of Engineering, Bengaluru, 560019, India

Abstract: A multimodal biometric system using feature fusion and hybrid fusion of face and iris is proposed. A novel feature level fusion of face and iris features, using both low and high frequency sub-bands of discrete wavelet transform (DWT), and principal component analysis (PCA) is designed. The redundant data resulted from feature fusion of face and iris is overcome by feature transformation through linear discriminant analysis (LDA). The proposed feature level fusion is tested for face databases (ORL and Yale), and iris databases (CASIA and UBIRIS). The performance of the proposed feature level fusion approach is superior to DWT, PCA and Gabor+PCA-based fusion methods by exhibiting highest recognition rate of 97% with low dimensionality. Further, a hybrid fusion of feature level and score level fusion methods is proposed to improve the performance of the multimodal biometric system. In comparison to feature level and score level fusion methods, the hybrid fusion method attains highest recognition rate of 99.6% and least equal error rate (EER) of 0.086 for ORL+CASIA database.

Keywords: multimodal biometrics; feature level fusion; sub-band fusion; discrete wavelet transform; DWT; principal component analysis; PCA; linear discriminant analysis; LDA; hybrid fusion.

DOI: 10.1504/IJBM.2020.110810

International Journal of Biometrics, 2020 Vol.12 No.4, pp.357 - 376

Received: 28 Sep 2019
Accepted: 11 Feb 2020

Published online: 29 Oct 2020 *

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