TITLE OF SEMINAR

A SEMINAR REPORT
submitted by

NAME OF STUDENT
REGISTER NO

to

the APJ Abdul Kalam Technological University

in partial fulfillment of the requirement for the award of the Degree

of

Bachelor of Technology
in
Computer Science and Engineering

Under the guidance of

Dr./Prof./Mr./Ms. NAME OF FACULTY
Assistant Professor/Associate Professor/Professor

Department of Computer Science and Engineering

Amal Jyothi College of Engineering(Autonomous)
Kanjirappally-686518

December 2024
 DEPT. OF COMPUTER SCIENCE AND ENGINEERING

AMAL JYOTHI COLLEGE OF ENGINEERING(AUTONOMOUS)

KANJIRAPPALLY

CERTIFICATE

This is to certify that the seminar report entitled “ Seminar Title ” submitted by Student
Name (Register No.) to the APJ Abdul Kalam Technological University in partial fulfillment
of the requirements for the award of the Degree of Bachelor of Technology in Computer Sci-
ence and Engineering during the year 2024-2025, is a bonafide work carried out by him/her
under my guidance and supervision.

Dr./Prof./Mr./Ms. Guide Name Ms. Elisabeth Thomas Ms. Shiney Thomas

Internal Supervisor Seminar Coordinators

Dr.Juby Mathew

Head of Department, CSE

 ACKNOWLEDGEMENT

First of all I sincerely thank the Almighty GOD who is most beneficent and merciful for
giving me knowledge and courage to complete the seminar successfully.

I derive immense pleasure in expressing my sincere thanks to our Manager, Rev. Fr. Boby
Alex Mannamplackal, to our Director(Administrator), Fr. Dr. Roy Abraham Pazhaya-
parampil and to our Principal, Dr. Lillykutty Jacob for their kind co-operation in all aspects
of my seminar.

I express my gratitude to Dr. Juby Mathew, HOD, Department of Computer Science and
Engineering his support during the entire course of this seminar work.

I express my sincere thanks to Ms. XYZ, my internal guide and Ms. Elisabeth Thomas and
Ms. Shiney Thomas, our Seminar Co-ordinators for their encouragement and motivation
during the seminar.

I also express my gratitude to all the teaching and non teaching staff of the college espe-
cially to our department for their encouragement and help during my seminar.

Finally I appreciate the patience and solid support of my parents and enthusiastic friends
for their encouragement and moral support for this effort.

NAME OF STUDENT

i
 ABSTRACT

Integrity is the practice of being honest and showing a consistent and uncompromising ad-
herence to strong moral and ethical principles and values. In ethics, integrity is regarded as
the honesty and truthfulness or accuracy of one’s actions. Integrity can stand in opposition to
hypocrisy, in that judging with the standards of integrity involves regarding internal consis-
tency as a virtue, and suggests that parties holding within themselves apparently conflicting
values should account for the discrepancy or alter their beliefs. The word integrity evolved
from the Latin adjective integer, meaning whole or complete. In this context, integrity is
the inner sense of “wholeness” deriving from qualities such as honesty and consistency of
character. As such, one may judge that others “have integrity” to the extent that they act
according to the values, beliefs and principles they claim to hold.

An individual’s value system provides a framework within which the individual acts in
ways which are consistent and expected. Integrity can be seen as the state or condition of
having such a framework, and acting congruently within the given framework.One essential
aspect of a consistent framework is its avoidance of any unwarranted (arbitrary) exceptions
for a particular person or group—especially the person or group that holds the framework.
In law, this principle of universal application requires that even those in positions of official
power be subject to the same laws as pertain to their fellow citizens.

ii
 CONTENTS

ACKNOWLEDGEMENT i

ABSTRACT ii

ABBREVIATIONS vii

1 INTRODUCTION 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Report Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 LITERATURE SURVEY 2
2.1 Discussion of earlier/older works . . . . . . . . . . . . . . . . . . . . . . . 2
2.2 Create appropriate subheadings . . . . . . . . . . . . . . . . . . . . . . . . 2

3 PROPOSED SOLUTION/METHODOLOGY 4
3.1 Add required subtopics as per paper . . . . . . . . . . . . . . . . . . . . . 4
3.1.1 Xyz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2 Xy Z . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2.1 Xy Z . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2.2 Xy Z . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4 FINDINGS AND ANALYSIS 6

5 CONCLUSION AND FUTURE WORK 8

iii
REFERENCES 9

iv
 LIST OF TABLES

4.1 Correct Rate and Error Rate obtained for CNN with feature vector as input . 6

v
 LIST OF FIGURES

4.1 Graphical representation of gradient value obtained by each materials in the

(ii) scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

vi
 ABBREVATIONS

WAT Wave Atom Transformation

LPQ Local Phase Quantization
CNN Convolutional Neural Network
LBP Local Binary Pattern
PCB Printed Circuit Board
BSIF Binarized Statistical Image Features
LivDet Liveness Detection
VGG Visual Geometry Group

vii
 CHAPTER 1

INTRODUCTION

1.1 Overview

Biometric security is a mechanism which is used to authenticate and ....

1.2 Motivation and Objectives

..To detect these fingerprint spoofing .... etc.

1.3 Report Organization

This section gives the organization of the report work. Overall, this report work gives a clear
and thorough study about the existing study of the proposed system and gives a description
about the implementation details. The second chapter gives a detailed description about the
literature survey. Third chapter gives a brief idea about the implementation details including
hardware and software requirements, outline of the proposed methodology. Fourth chapter
depicts the Results and Discussions and fifth Chapter gives the Conclusion and Future Work.
The Appendix shows the Screen Shots and Sample Code of the proposed systems.

1
 CHAPTER 2

LITERATURE SURVEY

2.1 Discussion of earlier/older works

The Fingerprint Recognition System has ubiquitous deployment in many day-to-day applica-
tions, such as financial transactions, international border security, unlocking a smart phone[1]

Introduction The Fingerprint Recognition System has ubiquitous deployment in many

day-to-day applications, such as financial transactions,[4] international border security, un-
locking a smart phone, etc.

2.2 Create appropriate subheadings

This paper proposes a method to obtain the discriminant features for fingerprints whether
they are real or spoofed, by performing feature level extractions [10]. The fingerprint im-
age is preprocessed using five preprocessing operations. The preprocessing operations were
selected which have the lowest validation errors.

1. Image reduction with different ratios Images are reduced using “Bilinear Interpola-
tion”. They are used to verify the effect of resolution on the classifier performance.

2. Region Of Interest (ROI) Many fingerprints from some datasets are not centered; back-
ground represents large part of image. To input the classification system the largest
area that comprises foreground fingerprints, a simple ROI method is used.

2
 Chapter 2. LITERATURE SURVEY

They are used to verify the effect of resolution on the classifier performance.They are used to
verify the effect of resolution on the classifier performance. They are used to verify the effect
of resolution on the classifier performance. They are used to verify the effect of resolution
on the classifier performance. They are used to verify the effect of resolution on the classifier
performance.

Department of CSE,Amal Jyothi College of Engineering 3

 CHAPTER 3

PROPOSED SOLUTION/METHODOLOGY

3.1 Add required subtopics as per paper

3.1.1 Xyz

Research Methods: Description of the methods and techniques used in the paper.
Data Collection: How data was gathered, if applicable.
Experimental Setup: Details of any experiments or simulations conducted.
Key Topics: Detailed discussion of the main topics covered in the paper.
Figures/Tables: Include and describe any figures, tables, or charts used in the paper for better
understanding.

MATLAB supports graphical user interface (GUI) features for developing applications.
For graphically designing these GUIs, MATLAB includes a GUIDE (GUI development en-
vironment) which also..

3.2 Xy Z

3.2.1 Xy Z

From these, only Digital-Persona was selected for the training and testing process in order
to reduce the time required for processing. It consists of 1000 live images in both training as

4
 Chapter 3. PROPOSED SOLUTION/METHODOLOGY

well as testing sets whereas fake images are obtained from four materials ie. Ecoflex

3.2.2 Xy Z

Wave Atom Transformation for Image Enhancement The fingerprint images obtained
from the LivDet 2015 datasets may contain some noise.... and assigned it with two values 1
and 2. If the predicted value is 1, then it is considered as LIVE else it is FAKE.

Department of CSE,Amal Jyothi College of Engineering 5

 CHAPTER 4

FINDINGS AND ANALYSIS

This section gives as a detailed analysis of the proposed approach. Findings: Summary of
the key findings or results presented.
Summary of Key Findings: Major takeaways from the seminar.
Analysis: Critical analysis of the information presented, including insights and implications.
We evaluated the performance on three scenarios to compare which one provides better per-
formance.

Evaluation of CNN with feature vector as input In this setting, the fingerprint images in
the dataset are firstly enhanced using Wave Atom Transformation method in order to remove
the unwanted noise from the image pixels..... It took only 00:00:20 hrs at the time of testing
whereas other two methods took more time. Figure 4.1 shows the gradient value obtained in
each case. The performance Table 4.1 depicts that Ecoflex 00-50

Table 4.1: Correct Rate and Error Rate obtained for CNN with feature vector as input

Materials Used (Live and Fake) CR ER

Ecoflex 00-50 0.788 0.211
Gelatine 0.808 0.192
Latex 0.802 0.198
WoodGlue 0.808 0.191

6
 Chapter 4. FINDINGS AND ANALYSIS

Figure 4.1: Graphical representation of gradient value obtained by each materials in the (ii)
scenario

Department of CSE,Amal Jyothi College of Engineering 7

 CHAPTER 5

CONCLUSION AND FUTURE WORK

The main limitation of many of the existing anti-spoof methods is the poor quality of the
datasets obtained. The spoofing materials of poor image quality decreases the overall per-
formance of the system. So we need either a best hardware for image acquisition or a good
image processing method to obtain better results , but most of the hardware are expensive.

Here we proposed a method ..... The proposed WAT enhanced method yields an average
accuracy of 85.8%. The WAT enhanced image with minutiae features extracted, achieved
an average accuracy of 79.8%. We also computed the performance of CNN without any
enhancement on image as input and obtained an average accuracy of 91.6%.

8
 REFERENCES

[1] Tarang Chugh, Kai Cao, and Anil K. Jain, “Fingerprint Spoof Buster: Use of
Minutiae-centered Patches,” IEEE Transactions On Information Forensics And Secu-
rity, 2018.

[2] Manisha Redhu and Dr.Balkisha, “Fingerprint Recognition Using Minutiae Extrac-
tor,” International Journal of Engineering Research and Applications (IJERA),Vol. 3,
Issue 4, Jul-Aug 2013.

[3] Emanuela Marasco and Arun Ross, “A Survey on Anti-spoofing Schemes for Fin-
gerprint Recognition Systems,” ACM Comput. Surv.47, 2, Article 28, Nov 2014.

[4] Kumar Abhishek, Ashok Yogi, “A Minutiae Count Based Method for Fake Finger-
print Detection,” Second International Symposium on Computer Vision and the Internet
(VisionNet’15), 2015.

[5] Joshua J. Engelsma, Kai Cao, and Anil K. Jain, “RaspiReader: An Open Source Fin-
gerprint Reader Facilitating Spoof Detection,” Life Fellow, IEEE, arXiv:1708.07887v1
[cs.CV], 25 Aug 2017.

[6] Athos Antonelli, Raffaele Cappelli, Dario Maio, and Davide Maltoni, “Fake Finger
Detection by Skin Distortion Analysis,” IEEE Transactions on Information Forensics
and Security, Vol. 1, No. 3, Sep, 2006.

[7] Rodrigo Frassetto Nogueira1, Roberto de Alencar Lotufo,and Rubens Campos

Machado, “Fingerprint Liveness Detection using Convolutional Neural Networks,”
IEEE Transactions on Information Forensics and Security, 1556-6013 (c) 2015.

9
 REFERENCES

[8] Diego Gragnaniello, Giovanni Poggi, and Carlo Sansone and Luisa Verdoliva,
“Fingerprint Liveness Detection based on Weber Local Image Descriptor,” IEEE Jour-
nal, 2013.

[9] Luca Ghiani, Gian Luca Marcialis, and Fabio Roli, “Fingerprint Liveness Detec-
tion by Local Phase Quantization,” 21st International Conference on Pattern Recogni-
tion(ICPR 2012), Nov 2012.

[10] Samruddhi S. Kulkarni and Dr. Hemprasad Y. Patil , “A Fingerprint Spoofing De-
tection System Using LBP,” International Conference on Electrical, Electronics, and
Optimization Techniques (ICEEOT), 2016.

While binding, after this report add PPT

(printed two slides per page in landscape
mode. Then add the Seminar Base Paper.

Department of CSE,Amal Jyothi College of Engineering 10