Low and high frequency wavelet sub-band-based feature extraction
by D.V. Rajeshwari Devi; K. Narasimha Rao
International Journal of Biometrics (IJBM), Vol. 10, No. 3, 2018

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.

Online publication date: Mon, 09-Jul-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com