Real Time IOT based biometric extendable classroom attendance management system

Project Title

Real Time IOT based biometric extendable classroom attendance management system

Project Area of Specialization Artificial IntelligenceProject Summary

Real Time IOT based biometric extendable classroom attendance management system is a process of recognizing the faces and fingerprints for taking attendance by using face and fingerprint based on high - definition monitoring and other Computer technologies. To track attendance, we use an algorithm that compares facial and finger print traits and patterns.

Artificial intelligence is at the root of this. Detecting objects with Haar Cascade classifiers is a good idea. Paul Viola and Michael Jones developed this method in their paper Rapid Object Detection Using a Boosted Cascade of Simple Features. The technique I utilized in this project was Haar Cascade, which is a machine learning-based approach that uses a large number of positive and negative images to train the classifier.

Biometrics are used in the face recognition system to map face traits in an image. Fingerprint scanners function by capturing the pattern of ridges and valleys on a finger, while it compares the information with a known face database to get the same.

Both modules are connected to the internet, which means that data is saved in the backend on a server/cloud at the same moment a user is registered.

We recommended this idea to save time and avoid the need of proxies or bogus entries. We're putting in a portable finger print scanner in the main classrooms to double-check that every student's attendance is recorded and that no proxy is called. The facial recognition system will suffice in smaller classrooms.

Project Objectives

The goal of this project is to develop an automated system for recording student attendance both in class and in exams, which turns out to be a better alternative to the manual paper attendance recording system.
Real Time IOT based biometric extendable classroom attendance management system efficient, time-saving, simple and easy.

Project Implementation Method

We use Raspberry Pi system for implementation. We will install firmware on it and then use Python IDE. then for biometric fingerprint we use Nord MCU. Next, for face recognition, we use cameras that take pictures and compare them to the images available in the database.

Benefits of the Project

A management system in which attendance is recorded automatically. We overcome the problems of manual entry by adopting the proposed solution. We feel that this will save time and effort while increasing accuracy, and that we can trust this method. The amount of effort required by humans will be reduced, but accuracy will improve.

Technical Details of Final Deliverable

Artificial intelligence is at the root of this. Detecting objects with Haar Cascade classifiers is a good idea. Paul Viola and Michael Jones developed this method in their paper Rapid Object Detection Using a Boosted Cascade of Simple Features. The technique I utilized in this project was Haar Cascade, which is a machine learning-based approach that uses a large number of positive and negative images to train the classifier. Next we are also making a cloud data base which we will integrate with the system. 

Final Deliverable of the Project HW/SW integrated systemCore Industry ITOther Industries Education Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT)Sustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	70000
raspberry pi 4 model b	Equipment	1	40000	40000
NodeMCU ESP8266	Equipment	1	1500	1500
Wide angle camera	Equipment	4	6500	26000
jumpers wires, solding wire, solder	Miscellaneous	10	250	2500