Facial Recognition based smart Attendance system using CCTV
Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s). Attendance marking with conventional method
2025-06-28 16:27:12 - Adil Khan
Facial Recognition based smart Attendance system using CCTV
Project Area of Specialization Internet of ThingsProject SummaryAttendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).
Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However, these techniques lack the element of reliability.
In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology.
The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people.
This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.
Why this project is different from other attendance system we are training system model in such a way that once the system will start working it will automatically generate sessions of working.
- Facial detection: To detect the faces from human
- Facial recognition: To recognize faces and trained the model.
- Data comparison: compare the captured image in dataset
- Attendance marking: system automatically mark all student’s attendance through image processing.
- Desktop application: For user interface.
- Making and printing report: from database.
Implementation methodology:
Primary database creation and training:
The original database containing the images of the students is created by taking a live real time video of the students, and splitting the video into thirty frames, converting them to gray scale and storing only the faces of the students as images, then we will be training the respective images using the LBPH algorithm all the while storing their respective histogram value’s and then comparing the stored and trained images against the captured images to mark the attendance. The software used for splitting the video into frames is Open-CV.
Image Detection Phase:
In this second phase once the video has begun capturing, simultaneously the YOLO v4 algorithm is applied to the video to get individual faces of the students and obtaining the distinct features of their face(eyes, nose, ears and lips) by making use of line features and edge features, the YOLO v4 algorithm basically works by giving us the parts of the face that are needed most for detection i.e., the ROI (Region of Interest) and processing and cropping out other regions of the face that do not play a role in the image processing and matching part. Once the faces are detected they are extracted and stored.
Image Matching Phase:
In this third and most crucial phase of recognizing the student, that is comparing captured image against the stored images in the database, this method is done by making use of the LBPH algorithm (Local Binary Pattern Histogram), each image stored in the database has.
Mark attendance: The system will automatically mark the attendance of all the student who entered in the class.
Generates Reports: the system will automatically generate the excel file of attendees with date and time.
Benefits of Proposed system:
Foolproof:
Attendance marking becomes foolproof in nature, students cannot carry out the previous means of false proxies for their friends as the system needs faces of the students and nothing else.
Time saving:
Helps save time that at moments can get lost due to students disrupting the normal attendance marking method.
Efficient:
- Instead of teachers manually updating attendance to the university servers, the system will itself calculate attendance of students beforehand.
Our final product will be a device having following hardware and software components:
- Raspberry pi 4 B 8GB controller: For image processing.
- Pi Camera 16MP: For facial detection.
- 4s lippo battery 6000mah: Used for communication over GSM and/or internet.
- SD card: Raspberry pi board needs memory for processing
- SD card connector: to connect the SD with board
- Desktop interface: A desktop application analyze the data and generate results.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 69000 | |||
| Raspberry pi 4B 4GB board | Equipment | 1 | 30000 | 30000 |
| Pi Camera 16MP | Equipment | 1 | 12000 | 12000 |
| 4s lippo battery 6000mah | Equipment | 1 | 9000 | 9000 |
| 4s lippo battery charger | Equipment | 1 | 6000 | 6000 |
| Stationary | Miscellaneous | 1 | 5000 | 5000 |
| SD card | Equipment | 1 | 4000 | 4000 |
| SD card connector | Equipment | 1 | 3000 | 3000 |