Citation

Ong, Thian Song and William, Ardianto and Connie, Tee and Goh, Michael Kah Ong (2018) Robust hybrid descriptors for multi-instance finger vein recognition. Multimedia Tools and Applications, 77 (21). pp. 29163-29191. ISSN 1380-7501

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Abstract

Finger vein recognition is a type of biometric technology that uses the vein pattern inside the human finger as a personal identifier. In this paper, Local Hybrid Binary Gradient Contour (LHBGC) and Hierarchical Local Binary Pattern (HLBP) are proposed as the texture descriptors for finger vein recognition to increase the discriminant capability of the finger vein texture. LHBGC extracts both sign and magnitude components of the finger vein image for recognition, while HLBP utilizes the LBP uniform texture pattern of the vein image without any training required. Furthermore, a multi-instance biometrics that fuses multiple evidences from an individual has also been proposed to address the problem of noisy data. Multi-instance biometrics is the most inexpensive way to obtain multiple biometric evidences from a biometric trait without multiple sensors and additional feature extraction algorithms. Experiments on several benchmark databases validate the efficiency of the proposed multi-instance approach. An equal error rate as low as 0.00002% is achieved using the combination of three fingers at score level fusion.

Item Type:	Article
Uncontrolled Keywords:	Robust,biometrics,finger vein recognition,multi-instance recognition,hybrid descriptors,local hybrid binary gradient contour
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	21 Mar 2021 19:23
Last Modified:	21 Mar 2021 19:24
URII:	http://shdl.mmu.edu.my/id/eprint/7474

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