SQL Based Database for Attendance Management System
The essential thought of our project is to design a database for a facial recognition attendance system. Furthermore, we will analyze and accurate our data by applying machine learning algorithms. We will be doing this by utilizing SQL server machine learning services with python.
2025-06-28 16:29:38 - Adil Khan
SQL Based Database for Attendance Management System
Project Area of Specialization Computer ScienceProject SummaryThe essential thought of our project is to design a database for a facial recognition attendance system. Furthermore, we will analyze and accurate our data by applying machine learning algorithms. We will be doing this by utilizing SQL server machine learning services with python.
Main objective is to design a system for
- Face recognition utilizing machine learning.
- Feature extraction.
- Face classification.
Step-1
We need to store and access students' data (student names, reg numbers, face identity) for the attendance system, for this purpose we could choose between file system ,excel sheets and database. So, we prefer database because of its ability to store information more efficiently, database can handle numbers of volumes of information that is not manageable in excel sheet and file system.
Step-2
We need to compare student's face features with database facial and image data to mark attendance. To meet this challenge. We use machine learning's algorithms to analyze, precise, accurate and make our data more efficient. We compare student face features with image ,facial database more efficiently and intelligently.
Our project is focused on dealing with machine learning and database technologies. Aside from Computer Science, this field can also be utilized in various other fields (online transactions, banks, retail, websites, and warehouses). All businesses need a database to store their data. Machine learning is a set of algorithms that automatically collects and interprets data. It is expected to grow at a fast pace and reach $117 billion by 2027.
1. Data preparation/registration.
2. Completion of the database structure.
3. Database table designs.
4. Train machine learning model on data.
5. Analysis/Evaluation of machine learning model outcomes.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 70000 | |||
| SQL SERVER | Equipment | 1 | 70000 | 70000 |