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. 2011;11(12):11357-71.
doi: 10.3390/s111211357. Epub 2011 Nov 30.

Finger vein recognition using local line binary pattern

Bakhtiar Affendi Rosdi  1 , Chai Wuh Shing, Shahrel Azmin Suandi
Affiliations

Finger vein recognition using local line binary pattern

Bakhtiar Affendi Rosdi et al. Sensors (Basel). 2011.

Abstract

In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).

Keywords: biometrics; finger vein; hand-based biometrics; local binary pattern; local derivative pattern; local line binary pattern.

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Figures

Figure 1.
Figure 1.
The finger vein image (left), the image after processed by LBP (middle) and the image after processed by LLBP (right).
Figure 2.
Figure 2.
Block diagram of the proposed method.
Figure 3.
Figure 3.
Finger vein image capturing device.
Figure 4.
Figure 4.
Example of (a) the captured images, (b) the binarized images with the center of the objects and (c) the cropped images for a finger at intervals.
Figure 5.
Figure 5.
The resized (top) and their enhanced images.
Figure 6.
Figure 6.
Example of LLBP operator.
Figure 7.
Figure 7.
Examples of the captured finger vein images.
Figure 8.
Figure 8.
EERs (%) by varying S for LBP (8, 1) based on a sub-dataset of finger vein images.
Figure 9.
Figure 9.
EERs (%) by varying S for LBP (8, 2) based on a sub-dataset of finger vein images.
Figure 10.
Figure 10.
EERs (%) by varying S for LDP based on a sub-dataset of finger vein images.
Figure 11.
Figure 11.
EERs (%) by varying S for LDiP based on a sub-dataset of finger vein images.
Figure 12.
Figure 12.
EERs (%) by varying S for LTP based on a sub-dataset of finger vein images.
Figure 13.
Figure 13.
EERs (%) according to various operators based on the whole dataset of finger vein images.
Figure 14.
Figure 14.
Example of (a) the cropped images and the images after processed by various texture descriptors ((b) LBP (8, 1), (c) LBP (8, 2), (d) LDP, (e) LDiP, (f) LTP, (g) LLBP, (h) LLBPh, and (i) LLBPv) for three different fingers.
See this image and copyright information in PMC

References

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