Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.
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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
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APA
PAUL, A. K. (2024). Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.. Michael Okpara University of Agriculture. Retrieved June 7, 2026, from http://repository.mouau.edu.ng/works/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2
MLA
PAUL, Akoma Knowledge. "Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.." Michael Okpara University of Agriculture, 10 Jan. 2024, http://repository.mouau.edu.ng/works/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2. Accessed June 7, 2026.
Chicago
PAUL, Akoma Knowledge. "Face Recognition And Qrcode Attendance Taken And Verification System Using Deep Learning Approach:-Akoma, Knowledge P.." Michael Okpara University of Agriculture (2024). Accessed June 7, 2026. http://repository.mouau.edu.ng/works/face-recognition-and-qrcode-attendance-taken-and-verification-system-using-deep-learning-approach-akoma-knowledge-p-7-2