Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and authentication systems.

1. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
195
A Comprehensive Approach for Multi Biometric
Recognition Using Sclera Vein and
Finger Vein Fusion
N. Prasath
PG Scholar, Department of CSE,
Paavai College of Engineering,
Namakkal, India.
M. Sivakumar
Assistant Professor, Department of
Paavai College of Engineering,
Namakkal, India.
CSE,
Abstract— Sclera and finger print vein fusion is a new biometric approach for uniquely identifying humans. First, Sclera vein is
identified and refined using image enhancement techniques. Then Y shape feature extraction algorithm is used to obtain Y shape
pattern which are then fused with finger vein pattern. Second, Finger vein pattern is obtained using CCD camera by passing
infrared light through the finger. The obtained image is then enhanced. A line shape feature extraction algorithm is used to get line
patterns from enhanced finger vein image. Finally Sclera vein image pattern and Finger vein image pattern were combined to get
the final fused image. The image thus obtained can be used to uniquely identify a person. The proposed multimodal system will
produce accurate results as it combines two main traits of an individual. Therefore, it can be used in human identification and
authentication systems.
Index Terms— Sclera vein recognition, finger print vein recognition, vein identification, features fusion, multimodal system.
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1 INTRODUCTION
The white part of the eye is called sclera. Sclera veins can
be captured and analysed using image processing techniques.
The vein pattern of eye does not change with age, eye infection
and alcohol intake. So it can be used to verify the identity of a
person. In some cases sclera vein pattern may not be reliable and
hence multi biometric approach is recommended.
A new method which combines sclera vein recognition and
finger vein recognition to provide better authenticity is
proposed. Even though many multimodal systems are available,
the combination of sclera and finger vein is different which
provides positive and satisfying results. The proposed algorithm
extracts features from both sclera and finger vein image and
combines to form a final image. The fusion is applicable only if
the two extracted patterns are compatible.
2 BACKROUND OF VEIN PATTERN
RECOGNITION
2.1 Overview of Sclera Vein Recognition
Sclera vein recognition approach involves sclera image
identification, segmentation, enhancement, feature extraction.
Sclera image segmentation is the first step in sclera vein
recognition. It can be obtained with the help of a high definition
digital camera. The next step is sclera segmentation.
After sclera segmentation, it is necessary to enhance and
extract the sclera features since the sclera vein patterns often lack
contrast, and are hard to detect. Sclera vein appears to be in Y
shape pattern. Y shape line descriptor algorithm is used to extract
the features from enhanced sclera image. The flow of sclera vein
recognition process is shown in fig. 1.
Fig. 1. Simplified Sclera Image extraction process.

2. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
196
2.2 Y-shape Feature Extraction
A novel line-descriptor method is used to describe and store
the extracted vein pattern for recognition. The goal is to reliably
extract and describe the vein pattern in the sclera for use in
identifying the user. As the present set of vessel segments
combine to create Y shape branches often belonging to same
sclera layer. When the numbers of branches is more than three,
the vessels branches may come from different sclera layers and
its pattern will deform with movement of eye. Y shape branches
are observed to be a stable feature and can be used as sclera
feature descriptor.
Fig. 2. Enhanced sclera image with Y shape patterns.
To detect the Y shape branches in the original template, a
search for the nearest neighbours set of every line segment in a
regular distance should be conducted and the angles among these
neighbours should be classified. If there were two types of angle
values in the line segment set, this set may be inferred as a Y-
shape structure and the line segment angles would be recorded as
a new feature of the sclera.
There are two ways to measure both orientation and
relationship of every branch of Y shape vessels:
1) One is to use the angles of every branch to x axle,
2) The other is to use the angels between branch and iris
radial direction. The first method needs additional rotation
operating to align the template.
3 PROPOSED MULTIMODALSYSTEM
Proposed Multimodal system is a fusion of two individual
biometric systems. It combines the benefits of both sclera vein
and finger vein authentication systems. Finger vein
authentication is a new biometric approach which verifies one’s
identity using finger vein pattern.
3.1 Finger Vein Recognition
As the foremost step finger vein pattern should be captured.
To do so, a good quality CCD camera and an infrared light
emitter can be used. As the infrared lights get absorbed by
blood, the vein pattern appears to be a series of dark lines.
Then the image is segmented using localized Radon transform
(LRT) algorithm.
Fig. 3. A typical way of capturing finger vein pattern.
3.2 Repeated Line Tracking
The repeated line tracking algorithm is used in finger vein
identification. The idea is to trace the veins in the image by
choosing directions according to predefined probability in the
horizontal and vertical orientations. The starting vein point is
randomly selected and the whole process is repeated for certain
number of times.
Fig. 4. Finger vein image extraction process.
A typical finger vein feature extraction process has four steps
as mentioned in fig. 4. The foremost process is capturing the
finger vein using a CCD camera and Infrared light Emitter. After
that the captured image is segmented and enhanced. Enhancing
the image helps us to identify the vein pattern clearly. The final
image has the entire necessary feature that is required to identify
a person uniquely.

3. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
197
Fig. 5. Enhanced finger vein image with repeated lines.
Image fusion is performed by combining the biometric
template extracted from every pair of fingerprints and eye
representing a user. The matching score is calculated through the
Hamming distance calculation between two final fused
templates. The template obtained in the encoding process will
need a corresponding matching metric that provides a measure of
the similarity degree between the two templates. The result of the
measure is then compared with an experimental threshold to
decide whether or not the two representations belong to the same
user.
As this method produces exact matching score results and
provides dual security it is preferable in many mission critical
applications. It can be successfully implemented in low cost and
it is expected to give maximum protection by providing unique
human identity.
Fig. 6. Concatenation of sclera vein and finger vein patterns.
A simple concatenation process flow is shown in Fig 6.
Both sclera and finger vein images are fused together to form a
single image. The final template has the unique properties from
sclera as well as finger vein pattern. Therefore, it can be used to
find the identity of a person and adopted in security systems as
well.
4 DISCUSSION AND CONCLUSION
In this paper a new Multimodal biometric authentication
method is proposed and analyzed. The new method employs
security and special features of both sclera vein pattern and
finger vein pattern. Hence, dual security can be achieved. The
final image obtained after feature level fusion has the properties
of both finger vein and sclera vein. Therefore, it is less prone to
forgery. In many Real time environments, multi biometric
approach becomes essential and the proposed method better
serve the purpose.
REFERENCES
[1] Antikainen J., Havel J., Josth R., Herout A., Zemcik P. and
Hauta-Kasari M. (Feb. 2011), “Nonnegative tensor
factorization accelerated using GPGPU,”IEEE Trans.
Parallel Distrib. Syst., vol. 22, no. 7, pp. 1135–1141.
[2] Crihalmeanu S. and Ross A. (Oct. 2012), “Multispectral
scleral patterns for ocular biometric recognition,” Pattern
Recognit. Lett., vol. 33, no. 14,pp. 1860–1869.
[3] Cirean D. C., Meier U., Gambardella L. M. and
Schmidhuber J. (2010), “Deep, big, simple neural nets for
handwritten digit recognition,” Neural Comput., vol. 22,
no. 12, pp. 3207–3220.
[4] Cuevas C., Berjon D., Moran F. and Garcia N. (Feb. 2012),
“Moving object detection for real-time augmented reality
applications in a GPGPU,” IEEE Trans. Consum. Electron.,
vol. 58, no. 1, pp. 117–125.
[5] Dixon P. R., Oonishi T. and Furui S. (2009), “Harnessing
graphics processors for the fast computation of acoustic
likelihoods in speech recognition,” Comput. Speech Lang.,
vol. 23, no. 4, pp. 510–526.
[6] Gustavo Poli and José Hiroki Saito,” Parallel Face
Recognition Processing Using Neocognitron Neural
Network And Gpu With Cuda High Performance
Architecture”
[7] Hongtao Xie, Ke Gao, Yongdong Zhang,” Efficient Feature
Detection And Effective Post-Verification For Large Scale
Near-Duplicate Image Search”
[8] Kaufman P. and Alm A.(2003), “Clinical application”,
Adler’s Physiology of the Eye.
[9] Oyster C.W. (1999), “The Human Eye: Structure and
Function”, Sunderland:Sinauer Associates.
[10] Oh K.S. and Jung K. (2004), “GPU implementation of
neural networks,” Pattern Recognit., vol. 37, no. 6, pp.
1311–1314.
[11] Poli G., Saito J. H., Mari J. F. and Zorzan M. R. (2008),
“Processing neocognitron of face recognition on high
performance environment based on GPU with CUDA
architecture,” in Proc. 20th Int. Symp. Comput. Archit.
High Perform. Comput., pp. 81–88.
[12] Rakvic R. N., Ulis B. J., Broussard R. P., Ives R. W. and
Steiner N. (Dec. 2009), “Parallelizing iris recognition,”
IEEE Trans. Inf. Forensics Security,vol. 4, no. 4, pp. 812–
823.

4. INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY
VOLUME 5 ISSUE 2 – MAY 2015 - ISSN: 2349 - 9303
198
[13] Reza Derakhshani, Arun Ross, Simona Crihalmeanu, “A
New Biometric Modality Based On Conjunctival
Vasculature”
[14] Selenick, I.W., 2002,”A new complex-directional wavelet
transform and itsapplication to image denoising”. In: IEEE
Internat. Conf. on Image Processing, vol. 3, pp. 573–576.
[15] Xu Y., Deka S. and Righetti R. (Dec. 2011), “A hybrid
CPU-GPGPU approach for real-time elastography,” IEEE
Trans. Ultrason., Ferroelectr. Freq.Control, vol. 58, no. 12,
pp. 2631–2645.
[16] Zhou Z., Du E.Y., Thomas N.L. and Delp E.J. (May 2012),
“A new human identification method: Sclera recognition,”
IEEE Trans. Syst., Man,Cybern. A, Syst., Humans, vol. 42,
no. 3, pp. 571–583.
[17] Zhou Z., Du E.Y., Thomas N.L. and Delp E.J. (Jul. 2013),
“A comprehensive multimodal eye recognition,” Signal,
Image Video Process., vol. 7, no. 4,pp. 619–631.
[18] Yong lin, Eliza Yingzi Du, Senior Member, IEEE, Zhi
Zhou, Student Member, IEEE, and N.Luke Thomas, “An
Efficient Parallel Approach For Sclera Vein Recognition,”
Feb. 2014.