Signature difficult for the attacker to distinguish

Verification using Neural Network

Abstract— A novel system for protecting fingerprint and palm
vein  privacy by combining two different fingerprint
and palm veins into a new identity. In the enrollment, two fingerprint and palm
veins are captured from two different fingers. We extract the minutiae
positions from one fingerprint and palm vein, the orientation from the other fingerprint
and palm vein, and the reference points from both fingerprint and palm veins.
Based on this extracted information and our proposed coding strategies, a
combined minutiae template is generated and stored in a database. In the
authentication, the system requires two query fingerprint and palm veins from
the same two fingers which are used in the enrollment. A two-stage fingerprint
and palm vein matching process is proposed for matching the two query fingerprint
and palm veins against a combined minutiae template. By storing the combined
minutiae template, the complete minutiae feature of a single fingerprint and
palm vein will not be compromised when the database is stolen. Furthermore,
because of the similarity in topology, it is difficult for the attacker to
distinguish a combined minutiae template from the original minutiae templates.
With the help of an existing fingerprint and palm vein reconstruction approach,
we are able to convert the combined minutiae template into a real-look alike
combined fingerprint and palm vein. Thus, a new virtual identity is created for
the two different fingerprint and palm veins, which can be matched using
minutiae-based fingerprint and palm vein matching algorithms. The experimental
results show that our system can achieve a very low error rate with FRR 0.4% at
FAR 0.1%. Compared with the state-of-the-art technique, our work has the advantage
in creating a better new virtual identity when the two different fingerprint
and palm veins are randomly chosen.


— Fingerpint ,Palm vein Rrecoginization,Matlab/Simulink.




a very large number of identification systems exist that are based on different
types of biometrics. This includes iris, fingerprint, retina, voice, face, palm
and vascular pattern recognition. This last one is quite a new emerging
technology in the biometric field, and has been gradually adopted over the
world. The idea of using the hand vascular pattern as a biometric was first
considered in the early 1990s but it wasn’t until 1997 that a commercial
product was developed. In 2000 it finally became popular when an application
was created for personal identification based on the vein pattern on the back
of the hand 1. Since its introduction, hand vein pattern technology has
expanded to fingers and palm based systems and was adopted in 2007 by the
International Standard Organization (ISO) where the storage and transmission of
vascular biometric images was standardized. The demand for secure
identification systems has increased exponentially over the last ten years.
These systems are required to be very reliable but also easy to use since their
application is no longer restricted to high-security facilities. The advantages
of hand vein pattern recognition are due to the fact that veins lie underneath
the skin, which makes them easily accessible for the system but also hard to
alter. In this perspective, the accessibility of vein pattern compared with
other biometrics and its ease of use have made it a very interesting
alternative for applications where a high level of security is required. It is
also a good alternative to biometric systems that require physical contactin
order to identify the individual, especially in environments such as hospitals
where hygiene has high priority. An identification system should be fast,
simple and secure and due to its desirable advantages, vein pattern technology
is being considered into various authentication solutions for use in public
places (access control, time and attendance, security, hospitals).The market
for hand vascular pattern technology is growing rapidly and today it is an area
of ongoing research that draws a lot of attention.


The 1 Yingbo Zhou and Ajay Kumar have
proposed a method for Human Identification Using Palm-Vein Images. This paper
presents two new approaches to improve the performance of palm-vein-based
identification systems and they are Holistic approaches using subspace learning
and Line/curve matching using vessel extraction. This approach performs very
well even with the minimum number of enrollment images (one sample for

Xuekui Yan , Wenxiong Kang , Feiqi Deng , Qiuxia Wu have proposed a method for
Palm vein recognition based on multi-sampling and feature-level fusion. To
address the unsatisfactory recognition performance of a single-sample approach
in single biometric systems, multi- algorithm approaches have been proposed to
ensure that richer feature information can be extracted for better recognition
performance. In this method we used a bidirectional matching algorithm instead
of unidirectional matching, is adopted for efficient mismatching removal.

 3 Jen-Chun Lee has proposed a novel
biometric system based on palm vein image. He consider the palm vein as a piece
of texture and apply texture-based feature extraction techniques to palm vein
authentication in his work. A 2-D Gabor filter provides the optimized
resolution in both the spatial and frequency domains, thus it is a basis for
extracting local features in the palm vein recognition. He proposed an
innovative and robust directional coding technique to encode the palm vein
features in bit string representation. The bit string representation, called
Vein Code, offers speedy template matching and enables more effective template
storage and retrieval. The similarity of two Vein Codes is measured by
normalized hamming distance. High accuracy has been obtained by the proposed
method and the speed of the method is rapid enough for real-time palm vein

Kuang-Shyr Wua , Jen-Chun Leeb , Tsung-Ming Loc, Ko-Chin Changd , Chien-Ping
Chang have proposed a secure palm vein recognition system. In this paper, a
directional filter bank involving different orientations is designed to extract
the vein pattern and the minimum directional code (MDC) is employed to encode
the linebased vein features in binary code. A total of 5120 palm vein images from
256 persons are used to verify the validity of the proposed palm vein
recognition approach. High accuracies (>99%) and low equal error rate
(0.54%) obtained by the proposed method show that proposed approach is feasible
and effective for palm vein recognition.

Mansi Manocha and Parminder Kaur have proposed a method for Palm Vein
Recognition for Human Identification Using NN ie. Neural network. The proposed
algorithm is an alternative to currently employed palm-vein identification
approaches that do not take advantage from the cross-level image measurements.
Further improvement in the performance from the proposed approaches using
feature discretization and image quality measurements is expected and suggested
for the further work on the largescale palm image databases with the help of
Neural Networks.

Gitanjali Sikka , Er. Vikas Wasson have proposed a method for Palm Vein
Recognition with Fuzzy-Neuro Technique. In this paper feature is extracted
using FuzzyNeuro is used to enhance the response time and accuracy of system .
The fuzzy-neuro technique is based on the combination of the fuzzy-logic (fuzzy
sets) and pattern recognition based feed forward neural network. This algorithm
has been made able to tackle the light variations, noise and other specific
image characteristics in the palmvein recognition systems.

Jing-Wein Wang , Tzu-Hsiung Chen have proposed a Building Palm Vein Capturing
System for Extraction . In this paper The performance of the accurate
extraction ratio is 93.35% but the major drawback of this system is extractions
due to bad quality of the palm vein pattern images system may lead to the fatal
errors of the process.


II. Flow Chart






Image segmentation is
the authorized process of dividing an image into multiple parts. This is
typically used to identify objects or other relevant information in digital
images. Image segmentation is the process of partitioning a digital image into
multiple segments (sets of pixels, also known as super pixels). The goal of
segmentation is to simplify or change the representation of an image into
something that is more meaningful and easier to analyze. Image segmentation is
typically used to locate objects and boundaries (lines, curves, etc.) in
images. More precisely, image segmentation is the process of assigning a label
to every pixel in an image such that pixels with the same label share certain
visual characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image or a set o contours extracted from the
image (see edge detection). Each of the pixels in a region is similar with
respect to some characteristic or computed property, such as color, intensity,
or texture. The original image is captured with the black unwanted background.
Including the background reduced the accuracy of the original image, because
the position of palm usually varies across different palm vein images, it is
necessary to image segmentation in region of interest (ROI) before feature
extraction and matching with database. When a palm is irradiating by the
uniform light source



?  Image Pre-processing

?  Finger Vein Verification

?  Pattern Extraction

?  Sobel Edge Detector

?  Pattern Recognition



is a common name for operations with images at the lowest level of abstraction
both input and output are intensity images. The aim of pre-processing is an
improvement of the image data that suppresses unwanted distortions or enhances
some image features important for further processing.


Finger Vein

Finger vein
recognition is a method of biometric authentication that uses
pattern-recognition techniques based on images of human finger vein
patterns beneath the skin’s surface. Finger vein recognition is one of many forms
of biometrics used to identify individuals and verify their identity.




methods, called partition of attribute values or grouping of attribute values,
can be applied to decision tables with symbolic value attributes. If data
tables contain symbolic and numeric attributes, some of the proposed methods
can be used jointly with discretization methods. Moreover, these methods are
applicable for incomplete data. The optimization problems for grouping of
attribute values are either NP-complete or NP-hard. Hence we propose some
heuristics returning approximate solutions for such problems

Sobel Edge

The Sobel
operator performs a 2-D spatial gradient measurement on an image and so
emphasizes regions of high spatial frequency that correspond to
edges. Typically it is used to find the approximate absolute gradient magnitude
at each point in an input grayscale image.



recognition is a branch of machine learning that focuses on the
recognition of patterns and regularities in data, although it is in some
cases considered to be nearly synonymous with machine learning.Pattern
recognition systems are in many cases trained from labeled “training”
data (supervised learning), but when no labeled data are available other
algorithms can be used to discover previously unknown patterns (unsupervised



novel system for fingerprint privacy protection by combining two fingerprints
into a new identity. In the enrollment, the system captures two fingerprints
from two different fingers. A combined minutiae template containing only a
partial minutiae feature of each of the two fingerprints will be generated and
stored in a database. To make the combined minutiae template look real as an
original minutiae template, three different coding strategies are introduced
during the combined minutiae template generation process. In the authentication
process, two query fingerprints from the same two fingers are required. A
two-stage fingerprint matching process is proposed for matching the two query
fingerprints against the enrolled template. Our combined minutiae template has
a similar topology to an original minutiae template. Therefore, we are able to
combine two different fingerprints into a new virtual identity by
reconstructing a real-look alike combined fingerprint from the combined
minutiae template.