Title: Low and high frequency wavelet sub-band-based feature extraction

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

Addresses: Department of ECE, BMS College of Engineering Bengaluru, 560019, India; Department of ECE, KS Institute of Technology, Bengaluru, 560109, India ' Department of EIE, BMS College of Engineering, Bengaluru, 560019, India

Abstract: In a biometric system, feature extraction is an important task for faster and efficient identification of a person. A new feature extraction method, sub-band PCA+LDA is proposed to extract distinct features from low frequency and high frequency wavelet sub-bands. The proposed method captures both local and global features of two biometrics under consideration, face and iris. The matching scores of face and iris are normalised using minmax and tanh techniques, and fused using sum rule, product rule and weighted sum rule. For unimodal systems, the proposed method gives better recognition rate in comparison to other existing methods, like DWT, DWT+PCA, DWT+LDA, local binary pattern and subspace LDA. The performance of the proposed multimodal biometric system is superior to unimodal system in terms of attaining maximum of 100% recognition rate and minimum equal error rate (EER) of 0.017 for standard biometric databases.

Keywords: multimodal biometrics; sub-band fusion; feature extraction; discrete wavelet transform; principal component analysis; linear discriminant analysis; matching score level fusion.

DOI: 10.1504/IJBM.2018.093641

International Journal of Biometrics, 2018 Vol.10 No.3, pp.255 - 271

Received: 09 Dec 2017
Accepted: 25 Apr 2018
Published online: 30 Jul 2018 *

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