The purpose of this project is to detect the face of a person (in our case a student) and mark their attendance. In human interactions, the face is the most recognizable factor as it contains important information about a person or individual. All humans have the ability to recognize individuals fro
BSCS
The purpose of this project is to detect the face of a person (in our case a student) and mark their attendance. In human interactions, the face is the most recognizable factor as it contains important information about a person or individual. All humans have the ability to recognize individuals from their faces. In this process, the student will provide us their information alongside their pictures, which will be stored in our database we can detect their (student) faces through face recognition software. The functional features of this system are: it will mark present when the face is detected, and vice versa, it will also send a warning message to the student if found absent for the fourth time, furthermore, it will drop the student from the given course on the seventh absence, if student pay the penalty then the administration of the institute will enroll them again in the dropped course. It will mark invalid student when another student is in class who isn't enrolled, admin of the system can check student report of attendance as well. The technology used in this project is image processing. The software stores the faces that are detected and automatically marks attendance. This is a much better and efficient manner of marking attendance and maintaining data than the manual entry of attendance in logbooks, as it is both a tedious and time-wasting activity costing money as well. The significance of this project is that it will save time, secondly, the product also provides accurate results in a user-interactive manner rather far better than the existing attendance and leave management systems.
Face recognition can be applied for a wide variety of problems like image and film processing, human-computer interaction, criminal identification, etc. This has motivated researchers to develop computational models to identify the faces, which are relatively simple and easy to implement. The existing system represents some face space with higher dimensionality and it is not effective too. The important fact which is considered is that although these face images have high dimensionality, in reality they span very low dimensional space. So instead of considering whole face space with high dimensionality, it is better to consider only a subspace with lower dimensionality to represent this face space. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. The Eigenface approach uses the Local Binary Pattern (LBP) algorithm for the recognition of the images. It gives us an efficient way to find the lower-dimensional space.
In this project, we are using
IDE: VS Code
Language: Python
Database: SQL Server
Algorithms: Local Binary Pattern(LBP)
Provides facility for the automated attendance of students.
Face detection is used to locate the position of face region and face recognition is used for marking the understudy's attendance. The database of all the students in the class is stored and when the face of the individual student matches with one of the faces stored in the database then the attendance is recorded
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| high resolution camera | Equipment | 1 | 70000 | 70000 |
| Total in (Rs) | 70000 |
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