Automated Attendance System using Facial Recognition
(https://github.com/Mudit7/Face-Recognition-based-Attendance-System)
TEAM 25
Team Number : 25
Team Introduction
Team Members
Prazwal Chhabra, 20171192
Team Members
Sai Manoj Attanti, 20171154
Vikram Keswani, 2019201059
Nabhiraj Jain, 2019201062
Project Introduction
Overview
- Current method of manually marking attendance in class can be disruptive to the class flow and precious time used for this process which can be spent on teaching important topics.
- To overcome these issues, biometric feature like facial recognition can be used which involves the phases such as image acquisition, face detection, feature extraction, face classification, face recognition and eventually marking the attendance.
Why automated attendance?
- Saving even 5 minutes of time used up in every class for attendance marking can give us around '2 hours' ( 25 classes x 5 minutes ), of extra teaching time in a semester.
- This time can be utilized for teaching some important topics.
What we did different from others?
What differentiates us?
- Unique registration mechanism that requires student to register just by uploading their ID-cards.
- A scalable and secure architecture.
Project Components and contributors
Load Balancer, ID registration, Hosting
Face Extraction, API , ID registration
System Details
Data Loading, ID registration
Face matching, Attendance Marker
Attendance and Image Database
ID Registration
- Create account by just uploading your ID card.
- The system will extract all the details like Name, Roll Number, Contact Details, Student Image, etc and stores in database.
- This also ensures that the data given is reliable.
- CTPN model is trained on 'ICDAR 2015' to detect the textboxes and optimized using Side Refinement for less error rate.
- Using IRE and CV relevant information is extracted.
- Contributions By :- Sourav, Prazwal, Manoj
ID Registration
- Name
- Roll Number
- Contact Details
- Other Information
Face Extraction
- The user of the system can provide a single image with all the students seated or multiple images.
- The face extraction module extracts faces of students from the image and sends the extracted faces to the 'Face Matching' module.
- The implementation uses 'Haar Cascade' based implementations.
- Contributions By:- Prazwal
Face Matching and Buffer Manager
- The Face Matching module runs on a distributed system to match faces extracted by face extraction module with the student images.
- We have used buffer for faster execution of this process.
- Contributions By:- Mudit, Nabhiraj
Face Matching
Database Operations
- There are mainly two databases in the system:
- Student Details Database ( SQL + BLOB storage)
- Attendance Record Database ( SQL )
- Contributions By:- Vikram, Manoj
Database Operations
- A microservices architecture approach was used for developing the project and different services were developed.
- Some of the services developed include :
- Sending/Receiving
- Upload Image, etc.
- Also some part of the project has been hosted on 'ngrock' server .
- Contributions By:- Saurav, Prazwal
Load Balancer, Network Operations and Hosting
- Network Operations like Sending and Receiving data, media, etc. were implemented as flask services.
- A Load Balancer was developed for scalability.
- Contribution By:- Prazwal, Sourav
Load Balancer, Network Operations and Hosting
Dataset Used
Dataset
- As classroom images dataset was not available, we used 'Lok Sabha' and 'Rajya Sabha' images dataset available on Lok Sabha TV and Rajya Sabha TV websites to train and test our system.
- Also a dataset was build from TV series like 'Young Sheldon', etc.
WHY?
Why?
- The Parliament images dataset provide a similar situation where MP are seated in a wide seating arrangement.
- Also images of MP are also available for face matching.
- The dataset also has candid images, which provide a classroom like situation.