03-08-2012, 11:29 AM
Personal Identification For Single Sample Using Finger Vein Location and Direction Coding
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Abstract
Recent years have seen a plenty of personal
identification methods with different biometrics such as finger
pattern, face, palm-print and vein. The majority of these methods
focus on complex image data projections and transforms in
Fourier space, wavelet space or other domains, which usually
bring heavy load in computation and difficult understanding in
perceptual intuition. Moreover, these methods, oriented to
multiple samples learning, are constricted usually in application.
Among so much biometrics, vein, as a living feature with high
anti-counterfeiting capability, has attracted considerable
attention. In this paper, we propose a structured personal
identification approach using finger vein Location and Direction
Coding(LDC). First of all, we design a finger vein imaging device
with near-infrared(NIR) light source, by which a database for
finger vein images is established. Subsequently, we make use of
the brightness difference in the finger vein image to extract the
vein pattern. Furthermore, finger vein LDC is proposed and
performed, which creates a structured feature image for each
finger vein. Finally, the structured feature image is utilized to
conduct the personal identification on our image database for
finger vein, which includes 440 vein images from 220 different
fingers. The equal error rate of our method for this database is
0.44%.
Keywords- Biometrics; Finger vein; Personal identification;
Feature extraction;Location and direction coding
I. INTRODUCTION
Biometric personal identification is an automatic
identification based on the digital information of people’s
physiological characteristics or behavior pattern such as face,
finger, palm-print, iris, vein and voice and gait. These
Biometric characteristics are naturally attached on people,
nearly invariant with time, and see significant variation
between different individuals. All these promise good
application prospect for this technology. Successful examples
include the widely used face recognition, fingerprint
recognition.
There are many distinguishable characteristics on the hand,
like fingerprint, palm-print, finger-knuckle-print, hand shape,
hand vein, finger vein. All these hand based characteristics
carry rich personal information, which can be used for
identification generally. Moreover, the small size and high
flexibility of the hand make the hand-based biometrics easy to
sample with small instruments and easy procedure. Therefore,
hand-based biometric has drawn wide attention, and become a
hot issue in this area.
Finger vein recognition is one of the hottest topics of handbased
biometric identification. Vein image can be sampled by
near-infrared light. The smaller size and the higher flexibility
make the finger vein image easy to sample. Since the finger
vein image is only available on living body, it is nearly free
from forgery. There has been some research on the finger vein
based recognition. The major technical issues on finger vein
recognition are vein shape extraction and feature extraction.
Methods like local threshold method and repeated line tracking
have been applied in vein extraction[1-3]. Methods like
template matching[1], structured features[4] and wavelet
transform[5-6] have been applied in feature recognition. Some
other hand-based biometric such as palmprint[4], palm vein[7],
finger-knuckle-print[8] or their combinations[9] have been
studied for many years. These methods, most of them are
oriented to multiple samples learning, are constricted usually in
application. By comparison , the methods based on single
sample learning are promising in future application.
In this paper, we propose a novel approach based on single
sample finger vein recognition. Our method simultaneously
conducts the segmentation of the vein image and the extraction
of vein feature. We utilize the difference in brightness to
segment the vein area (location features), as well as extracting
the vein direction information (direction features). Finally, the
vein location and direction features are coded for identification.
This paper is organized as follows. Section II introduces
our finger vein image sampling system. The preprocessing of
the vein image, including finger segmentation, image
enhancement, denoising and normalization are described in
Section III. Section IV describes our algorithm for vein
segmentation, finger vein location and direction coding and
identification. The experiment results are shown in section V,
and Section VI is the conclusion.