// ACADEMIC CONTENT PORTAL · Computer Science
Thesis Topic Ideas: E-Attendance System Using Facial Recognition
🎓 Academic Journey Progression Map
1. Introduction & Problem Statement
Overview: Design and implement an automated student/employee attendance system using computer vision facial detection and recognition algorithms linked to a web dashboard.
Background Context (Nepal): Manual paper attendance in Nepali colleges and offices is time-consuming and vulnerable to proxy marking. Automated face recognition streamlines attendance logging.
2. Research Objectives
- ›Train lightweight face detection and recognition model (OpenCV / dlib / FaceNet)
- ›Build real-time webcam video feed capture interface
- ›Integrate automated database logging matching detected face embeddings with student IDs
- ›Develop web portal dashboard for faculty attendance reporting and CSV export
- ›Achieve 92%+ recognition accuracy under variable indoor lighting conditions
3. Proposed Methodology
- Collect face image dataset of 50 student volunteers with 10 poses/lighting variations each
- Extract 128-d face embeddings using dlib / OpenCV facial landmark detector
- Implement k-NN or SVM classifier matching live frame embeddings to database records
- Build Next.js / React management dashboard backed by Node.js and MongoDB
- Evaluate accuracy, false acceptance rate (FAR), and latency per recognition frame
$ Worked Example / Sample Scenario
Sample Scenario: A student walks past the computer lab camera. The dlib pipeline detects facial landmarks, computes 128-d embeddings, matches with ID #2081-CS-042 in 0.4 seconds, and marks attendance on the college portal.
4. Thesis Chapter-by-Chapter Outline
Chapter 1: IntroductionTU/KU/NEB standard
Background, problem statement, research questions, objectives, scope, and significance
Chapter 2: Literature ReviewTU/KU/NEB standard
Computer vision architectures, HOG vs CNN face detection, and embedding matching models
Chapter 3: System Design & MethodologyTU/KU/NEB standard
System architecture, ER diagrams, embedding pipeline, and dataset preparation
Chapter 4: Implementation & ResultsTU/KU/NEB standard
Accuracy evaluation, confusion matrix, frame rate benchmarks, and web portal screenshots
Chapter 5: Conclusion & Future WorkTU/KU/NEB standard
Summary of findings, limitations under extreme low light, and mobile app export roadmap
5. Recommended Tools & Technologies
To implement the practical, technical, or analytical portions of this thesis topic, the following software tools, libraries, or APIs are recommended:
6. Core References & Academic Sources
- [1]IEEE Xplore / Google Scholar — Computer Vision Facial Recognition Papers <!-- VERIFY: needs confirmation -->
- [2]NepJOL — Applied AI in academic administrative automation
7. Frequently Asked Questions (FAQs)
Q: Does face recognition work with eyeglasses or face masks?
Using facial landmark embeddings focused on eye-bridge and forehead keypoints allows partial recognition under minor occlusion.
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