Face Recognition based Smart Attendance System Using IoT
Abstract : Face recognition based smart attendance system using IoT is a tool for recognizing the students face while taking attendance by using face biometrics based on monitor camera image capturing.
In our face recognition based smart attendance project, a raspberry pi system will be able to find and recognize human faces fast and precisely in images. The long-established method of calling name of each student is tedious and there is always a chance of proxy attendance.
The proposed system is based on face recognition to maintain the attendance record of students.
As the process of attendance taking starts the system takes pictures of the attendees and then applies face detection and recognition technique to the given image and the recognized students are marked as present and their attendance is updated with corresponding time, student name and register number. We have used deep learning techniques to develop this project.
In existing system, we used RFID, KEYPAD based security for door accessing system. Due to that system, there is no accurate security for existing system. There are many limitations due to the existing system. To overcome the existing system limitations, we integrate to produce latest technology call face recognition.
The present system of taking attendance is either by manual or by using finger impression as biometric parameter. Manual /Traditional attendance is usually taken by calling students by name which takes a lot of time and has a chance of errors and proxies which makes the analysis of student performance imprecise.
Prone to Errors: Manual systems are susceptible to human errors, such as incorrect entries or missed records, leading to inaccuracies in attendance data.
Time-Consuming: Recording attendance manually can be time-consuming for both staff and administrators, especially in large organizations.
Easily Tampered: Manual records can be altered or falsified more easily than digital systems, leading to potential fraud or misuse.
Lack of Real-Time Data: Attendance data is typically recorded in real-time, which means there's a delay in processing and analyzing this information. This can hinder timely decision-making.
Storage Issues: Physical records require storage space and can be prone to damage or loss due to mishandling, natural disasters, or other unforeseen events.
The proposed system is designed to capture the face of each student and to store it in the database for their attendance.
The face of the student needs to be captured in well-lit room so that student's face features can be detected, the seating and the posture of the student need to be recognized.
With this system, there is no need for the teacher to manually take attendance in the class because the system records a video and through image processing/image training the face is recognized and the attendance database is updated in a spreadsheet.
The proposed system uses Raspberry Pi as computer and a webcam for capturing the images.
Real-Time Data Collection: IoT-enabled systems can provide real-time updates on attendance, allowing for immediate monitoring and reporting.
Enhanced Accuracy: Advanced face recognition algorithms improve accuracy in identifying individuals, reducing the likelihood of errors and fraudulent attendance entries.
Scalability: IoT systems can easily scale to accommodate a large number of users and multiple locations, making them suitable for both small and large organizations.
Remote Monitoring: IoT integration allows for remote monitoring and management of the attendance system, enabling administrators to access data and control the system from anywhere.
Automation: Automates the attendance tracking process, reducing the need for manual intervention and administrative tasks. This can also minimize human error and manipulation.
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