ABSTRACT
Face Recognition and QRCode
Attendance taken and Verification system using deep Learning Approach, this is
an artificial intelligence platform for security purposes. The approach is a
system designed to improve the security system of financial institutions. The
project work focuses on two authentication systems face recognition and credit
card verification. The issue of theft, i.e. an authorized person having access
to the financial properties of another has been in great disaster to the
advancing digital system. The problem faced by credit card users is
vulnerability to a lot of privacy issues such as credit card parameters. This
may commonly occur when users give their credit card numbers to unfamiliar
individuals or when cards are lost. Our solution proposes a technique by which
the features extracted from the image clicked during the payment made by a user
on an e-commerce portal will be compared to the features from the training
dataset of the respective user. Features extracted from the Images stored in the
administrator database acts as the training data set for authentication purpose.
The project implementation employed the methodology of the spiral model of
software development life cycle with a reason that the system implements an
iteratively and the framework is python flask, the programing language used
python and the database model user is sqlite3. The web-based platform was
tested, and the administrator side registered the user, and take pictures and
datasets. The user’s credit card identification and facial recognition were
tested with more than two and it was able to identify them separately and give
them access to their respective dashboards/account.
TABLE OF CONTENT
Front Page i
Certification ii
Dedication iii
Acknowledgement iv
Abstract vi
Table of Content vii
List of Tables xi
List of Figures xii
CHAPTER 1:
INTRODUCTION
1.1 Background
of the Study 1
1.2 Statement
of the Problem. 6
1.3
Aims
and Objectives of the Study 6
1.4
Significances of the Study 7
1.5 Scope of
the Study 8
1.6 Definition
of Terms 8
CHAPTER 2:
LITERATURE REVIEW
2.1 Concept of
Face Identification and Verification 9
2.2 Conceptual
Framework 9
2.3 Theoretical
Framework 10
2.3.1 Facial Recognition
Technology 11
2.3.2 Biometrics and
Identification in a Global Web 13
2.4 Empirical
Framework 16
2.4.1 Face
Recognition Operation 16
2.4.2 FRS Tasks
and Verification 18
2.5 Summary of
Previous Related Literature Review 20
2.6 Knowledge
Gap 22
CHAPTER 3: SYSTEM
ANALYSIS AND RESEARCH METHODOLOGY
3.1 Analysis of the Existing System 24
3.1.1 Advantage of the Existing System 24
3.1.2 Disadvantage of the Existing System 25
3.2 Analysis of the Proposed System 25
3.2.1 Advantage of the Proposed System 27
3.3 Research Methodology 28
3.3.1 Data Collection Methods 32
3.3.2 Adopted Research Methodology 35
3.3.3 Component of the Adopted Research Methodology 36
3.3.4 System
Investigation 37
3.4 Justification of the Newly Proposed System 38
CHAPTER 4: SYSTEM
DESIGN AND IMPLEMENTATION
4.1 Objective
of the New System 40
4.2 Decomposition and Cohesion of the High-Level
Model 41
4.2.1 Main Menu 41
4.2.2 The
Sub-Menus 42
4.3
Specification 43
4.3.1 Database
Specification 44
4.3.2 Input/output
Format 47
4.3.3 Use Case
Diagram 49
4.3.4
Algorithmic Operational Process 50
4.3.5 Data
Dictionary 51
4.4 Flowchart 53
4.5 New System
Requirement 55
4.5.1 Hardware
Requirement 55
4.5.2 Software
Requirement 56
4.6 Program
Development 56
4.6.1 Choice of
Program Environment 56
4.6.2 Language
Justification 56
4.7 System
Testing 57
4.7.1
Testing Plan 57
4.7.2 Testing
Data 58
4.7.3 Actual
Test Result versus Expected Test Result 58
4.7.4
Performance Evaluation 59
4.7.5
Limitation of the System 59
4.8 System
Conversion 60
4.8.1
Changeover Procedure 60
4.8.2
Recommended Procedure 61
4.9 System
Security 61
4.10
Documentation 61
4.11 Project
Costing 62
CHAPTER 5: SUMMARY,
RECOMMENDATION, AND CONCLUSION
5.1 Summary 64
5.2 Recommendation 64
5.3 Conclusion 65
REFRENCES
APPENDIX I
LIST
OF TABLES
Table 1: of Literature Review 22
Table 2: Physical Structure of the New System Database 45
Table 3: Information Stored in New System Database 46
Table 4: Data Dictionary of New System 51
Table 5: Result Table using Test data. 58
Table 6: General
project Cost. 63
LIST
OF FIGURES
Figure 1: Face
Recognition Sample 3
Figure 2: Face
Recognition Processing 16
Figure 3 Face
Recognition Operation 19
Figure 4: Proposed System Authentication Process 27
Figure 5: The
Research Method used for Implementation 30
Figure 6:
Component of the New System 36
Figure 7: Main
Menu of New System 42
Figure 8: Physical Design of the New System
Database 44
Figure 9: account holder registration 48
Figure 10: Biometricregistration 48
Figure 11:
Output Format of the New System 49
Figure 12: Use
case diagram of the New System 50
Figure 13:
Program flowchart of New System 54
Figure 14: The
Complete System Architecture 55
AKOMA, P (2024). Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.. Mouau.afribary.org: Retrieved Nov 23, 2024, from https://repository.mouau.edu.ng/work/view/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2
PAUL, AKOMA. "Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P." Mouau.afribary.org. Mouau.afribary.org, 10 Jan. 2024, https://repository.mouau.edu.ng/work/view/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2. Accessed 23 Nov. 2024.
PAUL, AKOMA. "Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.". Mouau.afribary.org, Mouau.afribary.org, 10 Jan. 2024. Web. 23 Nov. 2024. < https://repository.mouau.edu.ng/work/view/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2 >.
PAUL, AKOMA. "Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P." Mouau.afribary.org (2024). Accessed 23 Nov. 2024. https://repository.mouau.edu.ng/work/view/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2