01 Project Synopsis
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BCA Project Report
BCA Facial Recognition Attendance Final Year Project Report
Get structured BCA Facial Recognition Attendance Final Year Project Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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02 Detailed Project Report
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03 Customized Project Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
Facial Recognition Attendance System is a final year project built with Python Flask, OpenCV, TensorFlow/Keras, and SQLite for students who want an AI-based college attendance web application. This final year project uses Haar cascade face detection, MobileNetV2 embeddings, and cosine similarity matching to identify students and mark attendance through a browser camera workflow. It includes separate portals for administrators, faculty, and students. Admins can manage departments, courses, semesters, sections, subjects, faculty, students, face datasets, system settings, attendance, notices, and reports. Faculty can mark or correct attendance for assigned classes and export reports. Students can view attendance history, download CSV reports, read notices, and request face-data refresh. With configurable attendance rules, reports, and camera-based marking, this project is suitable for a final year major project in Flask, OpenCV, TensorFlow, and computer vision.
Login Credentials
Administrator
- Username:
admin - Password:
admin123
Faculty after seed
adesai/faculty123rverma/faculty123
Student after seed
- Example roll number:
BEC24CS001 - Example username:
priya.sharma - Password:
student123 - Seed includes eight demo students:
BEC24CS001toBEC24CS008
Related & Popular Reports
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- Admin dashboard for this final year project with attendance overview and management access
- Admin profile and password change
- System settings for college name, attendance time window, minimum attendance percentage, face threshold, and duplicate cooldown
- Department, course, semester, and class section CRUD
- Subject management linked to course, semester, and optional class section
- Faculty account create, edit, activate/deactivate
- Faculty assignment to subject and class section
- Student create, edit, activate/deactivate, and delete
- Student roll number, username, password, email, mobile, and class section management
- Face enrollment image upload
- Student embedding re-encoding
- Face dataset view, regenerate embeddings, and delete embedding
- Attendance grid with filters
- Manual attendance add, edit, and delete
- Daily, monthly, subject, student, class, and defaulter reports
- Bulk attendance export as CSV
- Notice create, edit, and delete
- Student login in this final year project using roll number or portal username
- Student dashboard with today’s attendance, stats, recent activity, notices, and enrollment summary
- Personal attendance history with date filters
- Daily detail, subject-wise, and monthly attendance tabs
- CSV downloads for personal, subject, and monthly attendance
- College, department, and faculty notices
- Camera-based attendance marking through
/student/mark - Recognition-only mark attendance route without login where implemented
- Read-only enrollment identity
- Contact update with email and mobile
- Password update with minimum length rule
- Face registration refresh request with optional note
- Withdraw pending face update request
- Help and troubleshooting page
- Faculty dashboard in this final year project with scoped class metrics and notices
- My classes listing based on faculty assignments
- Class detail view for assigned sections/subjects
- Camera-based face attendance for assigned scope
- Manual attendance for assigned subject and section
- Correct mistaken attendance where permitted
- Filterable attendance view
- Class, subject, and student percentage reports
- CSV/XLSX export links where implemented
- Faculty-visible notices
- Face update request review
- Faculty profile update with email, mobile, and optional password change
- Public landing page included in this final year project
/api/markendpoint for base64 camera image attendance marking- SQLite database with lightweight migrations
- Face snapshots stored after successful marks
- Mean embedding files stored as
.npyper student - Required folders auto-created at runtime
- Bootstrap 5 and Bootstrap Icons UI
- Optional seed data with Indian-style college demo records
- Seed force option for resetting academic demo data
- Suitable for final year project demo, viva, source code review, and report preparation
- Educational disclaimer included for face recognition accuracy and production limitations
- Open the final year project folder.
- Create a virtual environment:
python -m venv .venv - Activate the environment:
- Windows:
.venv\Scripts\activate - macOS/Linux:
source .venv/bin/activate
- Windows:
- Install dependencies:
pip install -r requirements.txt - Optional: seed demo data:
python seed.py - To reset academic demo rows and embeddings, use only when needed:
python seed.py --force - Upload face enrollment photos from Admin → Students if camera recognition should work.
- Start the final year Flask project:
python app.py - Open:
http://127.0.0.1:5000 - Use localhost or HTTPS so browser camera access works.
README Included
Setup Support Available
Administrator
- Username:
admin - Password:
admin123
Faculty after seed
adesai/faculty123rverma/faculty123
Student after seed
- Example roll number:
BEC24CS001 - Example username:
priya.sharma - Password:
student123 - Seed includes eight demo students:
BEC24CS001toBEC24CS008