Multi-algorithm decision-level fusion using finger-knuckle-print biometric

Almahafzah, H. and Sheshadri, H. S. and Imran, M. (2014) Multi-algorithm decision-level fusion using finger-knuckle-print biometric. In: Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, 248 . Springer, New Delhi, pp. 39-47.

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Official URL: https://doi.org/10.1007/978-81-322-1157-0_5

Abstract

This paper proposed the use of multi-algorithm feature-level fusion as a means to improve the performance of finger-knuckle-print (FKP) verification. LG, LPQ, PCA, and LPP have been used to extract the FKP features. Experiments are performed using the FKP database, which consists of 7,920 images. Results indicate that the multi-algorithm verification approach outperforms higher performance than using any single algorithm. The biometric performance using feature-level fusions under different normalization techniques as well has been demonstrated in this paper.

Item Type: Book Section
Uncontrolled Keywords: Biometrics, Normalization, Algorithms, Finger knuckle prints, Multi-Algorithm, Multi-Biometric, Computer science, Decision level fusion, Feature-level fusions
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Arshiya Kousar
Date Deposited: 11 Jun 2019 09:41
Last Modified: 17 Dec 2019 11:12
URI: http://eprints.uni-mysore.ac.in/id/eprint/2923

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