International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Fake Multi-biometric Detection for Applications
of Fingerprint, Iris and Face Recognition
1
Preethi.V and 2Prof.S.Chidambaram
1
PG Scholar/Communication Systems and 2Assistant Professor,Department of ECE
Adhiyamaan College of Engineering,Hosur,India
ABSTRACT-Many organizations are using different kinds of
machine – driven person’s identifications systems that
improve the user’s needs, satisfaction, and Potency to secure
essential resource. In this project it gives the information on
the recent developments in person’s identification using
biometric technique. By using this technique we ensure to
identify a person weather he/she is real person or fake person.
The target is to extend the safety of biometric reorganization
frameworks, by adding liveness assessment in a simple, fast,
user friendly and non-intrusive manner. In this project it
gives information about different modalities like fingerprint,
face recognition, and iris to review against the various types
of vulnerabilities attacks. The proposed method presents a
very low level of complexity, that makes it suitable for realtime applications, using general image quality features
extract from one image (i.e., the same non inheritable for
authentication purposes) to differentiate between legitimate
and shammer samples. The experimental results obtained on
publicly available data sets of fingerprint, iris, and 2D face
shows the proposed method which is extremely competitive
compared with other state-of-the-art approaches and that the
analysis of the overall image quality of real biometric samples
reveals highly valuable information that will be very
efficiently used to discriminate them from pretend traits.
Keywords-Identification,
Liveness.
Potency,
biometric,
attacks,
I. INTRODUCTION
The term “Biometric” comes from the Greek
words bios (life) and metric (measure). Biometrics refers
to technologies that measure and analyze human body
characteristics like fingerprints, irises, hand movements
voice and facial patterns, for authentication functions. The
field of biometrics examines the distinctive physical or
behavioral traits that can be used to determine a person‟s
identity. In Recent years, machine-controlled person
identification is highly researched because for protected
access to computer, buildings, mobile phones and ATM‟S.
Person identification is the process of associating an
identity to an individual. Person identification techniques
are classified into three types such as knowledge based
approach, token based approach, and biometric based
approach.
ISSN: 2231-5381
A knowledge-based approach depends on
something an individual knows to make a personal
identification, like a password or a PIN.Token-based
approaches are based on something an individual have to
create a personal identification sort of a passport, driver‟s
license, ID card, master card, or keys. Once credit and
ATM cards are lost or stolen, an unauthorized user will
typically come up with the right personal codes. Although
many people still select easily guessed PIN‟s and
passwords like birthdays, phone numbers and social
security numbers. Biometric based systems use
physiological or behavioral features of an individual for
identification. Whereas, in Biometric based systems it
cannot be forged or purloined data. Knowledge based and
Token based approaches have many disadvantages like
countersign forgotten, or countersign was stolen by hackers
or unauthorized person, Tokens could also forgotten, lost,
stolen, or misplaced. Biometric like Face recognition, iris,
and fingerprint technology may solve this drawbacks of
above mentioned approaches. It gives several advantages
over classical security methods such as Password or
something we have like keycard, ID,etc.
In biometric system there is no need for the user
to remember or recollect the difficult PIN codes that could
be easily forgotten or to hold a key that could be lost or
Stolen. Biometric system conjointly has some of the
disadvantages like (i) lack of secrecy, (ii) biometric trait
cannot be replaced,(iii)vulnerable to external attacks that
may decrease their level of security,(iv)vulnerability points
will generally divided into direct & indirect attacks.
Biometric recognition is the automatic recognition of a
person based on one or more of these traits. The word
“biometrics” is also used to denote biometric recognition
methods..A biometric recognition system is a pattern
recognition system.
Throughout
biometric recognition, biometric
traits are measured and analyzed to determine a person‟s
identity. Biometric Authentication or verification is any
method that validates the identity of a user who wishes to
sign into a system by measuring some intrinsic
characteristic
of
that
user.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Fig.1 Biometric verification and biometric identification (in case of PC login)
Distinctiveness
Permanence
Collectability
Performance
Acceptability
Circumvention
Face
Universality
Biometric
Identifier
TABLE I
Comparison Table for biometric technologies
H
L
M
H
L
H
H
Fingerprint
M
H
H
M
H
M
M
Iris
H
H
H
M
H
L
L
There are a number of alternative problems that
should be considered, including:(i) performance, that refers
to the achievable recognition accuracy and speed, the
resources needed to achieve the desired recognition
accuracy and speed, as well as the operational an
environmental factors that have an effect on the accuracy
and speed;(ii)acceptability, that indicates the extent to
which people are willing to accept the use of a particular
biometric identifier (characteristic) in their daily lives;(iii)
circumvention, which reflects how easily the system may
be fooled using fraudulent method.
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Fig 2.A graphical representation of FAR and FRR errors, indicating the
CER
The FRR or False Rejection Rate is the
probability that the system incorrectly rejects access to an
licensed person, because of failing to match the biometric
input with a template or guide. The FRR is often expressed
as a percentage of valid inputs which are incorrectly
rejected. FAR and FRR are key metrics for biometric
solutions, some biometric devices allow to tune them so
that the system more quickly matches or rejects. Both are
important, but for more applications one of them is
considered. False Accept Rate is also known as False
Match Rate, and False Reject Rate is usually referred as
False Non-Match Rate. CER is the rate where both accept
and reject error rates are equal.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
Image(Face,
Iris,
Fingerprint )
Attack:
Image(Face,
Iris,
Fingerprint )
Sensor
HW-Based
LD
Feature Extractor
Synthetic
Samples
SW- Based LD
Rest of
Biometric
Recognition
System
Attack: Spoofing
Fig 3. Types of attacks detected by using Hardware and Software based Liveness Detection
II.LIVENESS DETECTION
III.EXISTING SYSTEM
Liveness detection is the ability to determine
whether a biometric sample is being provided by a humanbeing rather than from copy created using an artifact.
Special attention is paid to the liveness detection
techniques ,which use totally different physiological
properties to distinguish between real and pretend traits.
Liveness assessment methods have to satisfy certain
requirements like (i) the technique shouldn‟t harmful to the
user ,(ii)people shouldn‟t be reluctant to use it,(iii)result
ought to be produces in reduced interval of time, (iv)
should be cost effective (v) should have a good fake
detection rate. Liveness detection methods are classified
into two techniques:(i)Hardware – based techniques, (ii)
Software –based techniques.
A spoofing attack is a situation in which one
person or program successfully masquerades as another by
falsifying data and thereby gaining an illegitimate
advantage. The above two types of methods as its own
advantages and disadvantages over the other and
combination of each would be the foremost fascinating
protection approach to extend the safety of biometric
systems. In hardware based techniques it has good pretend
detection rate whereas software based techniques typically
more cost-effective and less harmful since the
implementations are transparent or clear to user .
In the present work we tend to propose a novel
software based „multi –biometric and multi –attack‟
protection method which overcomes the limitations
through the use of image quality assessment (IQA). Being
a software based approach it offers the benefits like fast ,
user friendly, In hardware based techniques it has good
pretend detection rate whereas software based techniques
typically more cost-effective and less harmful since the
implementations are
transparent or clear to user.
The drawback of fake biometric detection may be
seen as a two –class classification problem where an input
biometric sample needs to be assigned to one of two
classes: Real or Fake. Expected quality differences
between real and fake samples may include: degree of
sharpness, luminance level ,color, quantity of information
found in both type of images (entropy), structural
distortions or natural appearance. Following this “qualitydifference” statement, in the present work we have
tendency to explore the potential of general image quality
assessment as a protection method against different
biometric attacks. The key point of the process is to find a
set of discriminant features which permits to build an
appropriate classifier which gives the probability of the
image “realism” given the extracted set of features.
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Fig .4General Diagram for Biomertic recognition
Once the feature vector has been generated the
sample is classified as real or fake by using some
classifiers. In existing technique, they used Linear
Discriminant Analysis (LDA) and Quadratic Discriminant
Analysis (QDA) which is used as the classifiers to classify
real or fake.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
IV PROPOSED SYSTEM
DATABASE
INPUT
DATA
Template
SENSOR
FEATURE
EXTRACTION
MATCHER
Features
REAL / FAKE
Decision
Fig. 5 Architecture of Proposed Method
First, evaluate the multi – biometric dimension of
the protection methodology. That is, its ability to achieve
good fake detection rate when compared to other state-ofthe-art approaches, with completely different biometric
modalities like Fingerprint, Iris, Face. Second , evaluate the
multi attack dimension of the protection methodology i.e.,
its ability to notice not only spoofing attacks however
conjointly fallacious access tries dispensed with synthetic
or reconstructed samples.
ethod is user friendly, fast and harmless to the user. So it is
preferable for the real time applications.
V EXPERIMENTS AND RESULTS
The performance of the proposed liveness
detection scheme is validated on different database such as
ATVS ,a dataset captured at the biometric Recognition
Group ,Fingerprint Liveness Detection competition 2009 &
2013.The above two comprises datasets of real and fake
images.
When compared to the existing method we have a
tendency to reduce the algorithms used i.e., before they
used two algorithms LDA and QDA. Here, we used ANN
algorithm in order to that we can load the entire image
database into the program at the same time and it‟ll
compare with the database and it classifies the given image
is real or fake based on database. Images of three biometric
is given which is shown in the fig .input is given to the
feature extraction which will calculate the features
according to the image quality measures and then Matcher
is used to classify the image is real or fake.
In proposed method we have considered some of
the 6 metrics such as Mean Square Error, Signal to Noise
ratio, Peak Signal to Noise Ratio, Structural Content,
Maximum Difference, Average Difference. After
calculating all those metrics ANN Classifier is used with
Feed Forward Neural Network Algorithm in the MATLAB
2013 to differentiate the real or fake traits. This proposed
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Fig. 6 Input of Fingerprint Image
Fig. 7 Input Image Feature Vector
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
for different biometric such as Fingerprint, Face and
iris.The performance of 6 metrics of fingerprint, face and
iris is tabulated below.
Table II
Features of Fingerprint, Face and Iris Images
FEATURES
Mean Square
Error
Peak Signal
to Noise
Ratio
Signal to
Noise Ratio
Average
Difference
Structural
Content
Maximum
Difference
Fig.8 Filter the Input Image
FINGERPRINT
0.2552
FACE
0.0373
IRIS
0.02052
29.9971
38.3510
40.2032
5.9317
14.2705
15.93055
0.4114
0.2958
0.0403
1.0162
1.0047
1.0025
51
51
46
VI CONCLUSION
Image quality assessment for liveness detection
technique is employed to detect the real and fake
biometrics. Due to image quality measurements it is easy to
find out real and fake users. As a result of fake identities
always have some different features than original it always
contain different color, luminance levels, general artifacts,
amount of information and amount of sharpness, found in
both type of images, structural distortions or natural
appearance. Multi-biometric is a challenging system. It is
more secure than uni-biometric system. In this project the
three biometric systems that are face, iris and fingerprint
recognition are taken from publicly available database. It is
very competitive performance and to its multi-biometric
and multi-attack characteristics. The proposed method has
some other attractive features such as: it is simple, fast,
non-harmful, user friendly and cheap.
In future remaining metrics and performance are
yet to be calculated. Further evaluation on other image
based modalities e.g., palm print, hand geometry, vein and
also for video attacks can be used.
Fig. 9 Original and Distorted Image
Fig. 10 DFT and IDFT
REFERENCES
Fig.11 FFT to Magnitude and Phase
Among 25 metrics 6 metrics are calculated and
the results are evaluated. The same process is carried out
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International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 2 – March 2015
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Details
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