The document discusses the development of a multimodal biometric system that utilizes iris, fingerprint, and facial recognition to enhance security and accuracy in identity verification. It outlines various biometric characteristics, system methodologies, expected outcomes, and specific literature that supports the use of multimodal systems over unimodal systems due to their increased reliability. Furthermore, it mentions the software tools and techniques used for implementation, focusing on feature extraction and fusion methods.

1. “IRIS, FINGERPRINT AND FACE BASED MULTIMODAL SYSTEM FOR
IMPROVING THE ACCURACY AND SECURITY OFA SYSTEM”
GUIDED BY:-
Mr. Mukesh Azad
SRMSCET, Bareilly
SUBMITTED BY:-
Deep Kumar Sharma
M.Tech(S.E), IVth sem
SRMSCET, Bareilly
09/09/2015 1

2. CONTENTS:
• Introduction:
I) Biometrics (Unimodal).
II) Multimodal system.
• Problem statement.
• Literature review.
• Methodologies.
• Expected Result.
• Software/Tools.
• References.
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3. Introduction
I) Biometrics
• Is the term given to the use of
biological traits
OR
behavioral characteristics
to identify an individual.
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4. Characteristics
1) The common Physical characteristics are:
• Fingerprint
• Face
• Retina
• Iris
• Vein pattern, and
• Hand and finger geometry
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5. Characteristics
2) Behavioral characteristics are:
• Keystroke dynamics
• Voice
• Gait, and
• Signature dynamics
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6. II) Multimodal system
• However, even the best biometric traits till date are facing numerous problems
some of them are inherent to the technology itself.
• One way to overcome these problems is the use of multimodal system.
• A multimodal system uses multiple sensors for data acquisition.
• This allows capturing multiple samples of a single biometric trait and/or
samples of multiple biometric traits.
• This approach is enables to provide significant improvement over unimodal
biometric system in terms of higher accuracy.
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7. 09/09/2015 7
II) Multimodal system

8. Multimodal login system
.
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Face
Hand geometry
Fingerprint

9. Problem statement
• The security is an important aspect in our daily life whichever the system we
consider security plays vital role.
• The multimodal biometric person identification technique based on the pattern
of the human iris, fingerprint and face is well suited to be applied to access
control and provides strong e-security.
• Security systems having realized the value of multimodal biometric system for
two basic purposes: to verify or identify users.
• The multimodal biometric system is more secure biometrics system than the
unimodal system.
• In unimodal system single trait is used for the authentication so it can be easy to
crack the security of the unimodal system.
• To solve this problem we can use the multimodal system.
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10. Literature review
• P.U.Lahane, Prof. S.R.Ganorkar, “Fusion of Iris & Fingerprint
Biometric for Security Purpose”, International Journal of Scientific &
Engineering Research, August-2012.
Has focused on to develop a biometric identification system that
represents a valid alternative to conventional approaches. Multimodal
biometric identification system based on iris & fingerprint trait is
proposed. Typically in a multimodal biometric system each biometric trait
processes its information independently. The processed information is
combined using an appropriate fusion scheme. Successively, the
comparison of data base template and the input data is done with the help
of Euclidean-distance matching algorithm. If the templates are matched
we can allow the person to access the system. We are going to check FAR
& FRR with different threshold level.
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11. Cont.
• Dipti.S.Randive, Manasi.M.patil, "Iris and Fingerprint Fusion for
Biometric Identification ", International Journal of Computer
Applications, September 2013.
Has discussed that a biometric system which relies only on a single biometric
identifier in making a personal identification is often not able to meet the
desired performance requirement.
•Andrea F. Abate, Michele Nappi, Daniel Riccio, Gabriele Sabatino, "2D
and 3D face recognition: A survey", Elsevier, January 2007.
Says that the Government agencies are investing a considerable amount of
resources into improving security systems as result of recent terrorist events
that dangerously exposed flaws and weaknesses in today’s safety mechanisms.
Badge or password-based authentication procedures are too easy to hack.
Biometrics represents a valid alternative but they suffer of drawbacks as well.
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12. Cont.
• Ujwalla Gawande1, Anushree Sapre2, Apurva Jain3, Sanchita
Bhriegu4, Shruti Sharma5,“ Fingerprint-Iris Fusion Based Multimodal
Biometric System Using Single Hamming Distance Matcher ",
International Journal of Engineering Inventions, February 2013.
explained that in the real world applications, unimodal biometric systems
often face limitations because of sensitivity to noise, intra class invariability,
data quality, and other factors. Improving the performance of individual
matchers in the aforementioned situation may not be effective.
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13. Cont.
• Shradha Tiwari, Prof. J.N. Chourasia, Dr. Vijay S. Chourasia,"A
Review of Advancements in Biometric Systems ", International Journal
of Innovative Research in Advanced Engineering January 2015.
Has discussed that Biometric systems for today’s high security
applications must meet stringent performance requirements. In this paper,
we provide an overview of the fundamentals of biometric identification,
together with a description of the main biometric technologies currently in
use, all of them within a common reference framework.
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14. Methodologies
• The objective is to develop a multimodal system to increase the accuracy and
security of a system.
• The multimodal system usage the two or more traits as input data for
authentication and authorization of any person.
• There are some fusion techniques and some feature extraction techniques are
used.
• In the Multimodal system two modules are used for processing:
I) Enrollment Module.
II) Matching Module.
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15. I) Enrollment Module
.
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IRIS FINGERPRINT
ROI EXTRACTION
NORMALIZATION
GABOR FILTER
EXTRACTED FEATURES
TEMPLATE DATABASE

16. I) Enrollment Module(Cont.)
In the enrollment module the
biometrics traits used for input.
Region of Interest extracted from
the images.
Then Normalization performed
on the ROI images.
Use the Gabor filter for feature
extraction.
Extracted features stored in the
Template database.
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17. II) Matching Module
.
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IRIS FINGERPRINT
ROI EXTRACTION
NORMALIZATION
GABOR FILTER
EXTRACTED FEATURES
TEMPLATE DATABASE
MATCHING
RESULT

18. II) Matching Module(Cont.)
In the Matching module the
biometrics traits used for input.
Region of Interest extracted from the
images.
Then Normalization performed on
the ROI images.
Use the Gabor filter for feature
extraction.
Extracted features are used for
matching with the extracted features
stored in the Template database .
After matching found the result.
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19. Expected Result
• We use the biometric multimodal system to increase the security of a
system.
• In this we use the different fusion methods, Feature Extraction
techniques.
• On the basis of different threshold levels calculate the values of optimal
False Acceptance Rate (FAR) & False Rejection Rate (FRR), to improve
the system accuracy, reliability & security.
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20. Software/Tools
Image processing toolbox in MATLAB & DATABASE will be used for the
implementation.
MATLAB 7.9: -
• The version of MATLAB R2009b is developed at September 4, 2009.
• It is a multi-paradigm numerical computing environment and fourth-generation
programming language. A proprietary programming language developed
by MathWorks, MATLAB allows matrix manipulations, plotting of
functions and data, implementation of algorithms, creation of user interfaces.
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21. References
[1] P.U.Lahane, Prof. S.R.Ganorkar, “Fusion of Iris & Fingerprint Biometric for
Security Purpose”, International Journal of Scientific & Engineering Research, August-
2012.
[2] Dipti.S.Randive, Manasi.M.patil, "Iris and Fingerprint Fusion for Biometric
Identification ", International Journal of Computer Applications, September 2013.
[3] Ujwalla Gawande1, Anushree Sapre2, Apurva Jain3, Sanchita Bhriegu4, Shruti
Sharma5,“ Fingerprint-Iris Fusion Based Multimodal Biometric System Using Single
Hamming Distance Matcher ", International Journal of Engineering Inventions,
February 2013.
[4] Andrea F. Abate, Michele Nappi, Daniel Riccio, Gabriele Sabatino, "2D and 3D
face recognition: A survey", Elsevier, January 2007.
[5] Shradha Tiwari, Prof. J.N. Chourasia, Dr. Vijay S. Chourasia, “ A Review of
Advancements in Biometric Systems ", International Journal of Innovative Research in
Advanced Engineering January 2015.
[6] S.P.Khandait, Dr. R.C.Thool, P.D.Khandait, "Automatic Facial Feature Extraction
and Expression Recognition based on Neural Network ", International Journal of
Advanced Computer Science and Applications, January 2011.
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22. References
[7] Maheswari M A.P, Ancy S and EbenPraisyDevanesam.K, “Biometric
identification system for features fusion of iris and fingerprint ", Recent Research in
Science and Technology 2012.
[8] Vaibhavkumar J. Mistry, Mahesh M. Goyani, " A literature survey on Facial
Expression Recognition using Global Features ", International Journal of Engineering
and Advanced Technology, April 2013.
[9] Vaibhavkumar J. Mistry1, Mahesh M. Goyani2, " Facial Expression Recognition
using Gabor Filter by minimizing Feature Vector ",International Journal of Computer
Science and Management Research May 2013.
[10] P.U.Lahane, Prof. S.R.Ganorkar, “Efficient Iris and Fingerprint Fusion for Person
Identification ", International Journal of Computer Applications, July 2012.
[11] Samarth Bharadwaj, Mayank Vatsa and Richa Singh, " Biometric quality: a
review of fingerprint, iris, and face ", EURASIP Journal on Image and Video
Processing 2014.
[12] Pradnya M. Shende, Dr.Milind V. Sarode, Prof. Mangesh M. Ghonge, “A Survey
Based on Fingerprint, Face and Iris Biometric Recognition System, Image Quality
Assessment and Fake Biometric ", International Journal of Computer Science
Engineering and Technology April 2014.
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23. THANK YOU
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