Smart Attendance using face recognition
Face recognition technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling student attendance system. Face recognition-based attendance system is a process of recognizi
2025-06-28 16:35:05 - Adil Khan
Smart Attendance using face recognition
Project Area of Specialization Artificial IntelligenceProject SummaryFace recognition technologies have made many improvements in the changing world. Smart Attendance using Real-Time Face Recognition is a real-world solution which comes with day to day activities of handling student attendance system. Face recognition-based attendance system is a process of recognizing the students face for taking attendance by using face biometrics based on high - definition monitor video and other information technology. In my face recognition project, a computer system will be able to find and recognize human faces fast and precisely in images or videos that are being captured through a surveillance camera. Numerous algorithms and techniques have been developed for improving the performance of face recognition but the concept to be implemented here is Deep Learning. This work proposes a new methodology in which attendance of each individual student in a class room is automatically updated in a database by analyzing their faces and comparing them with the predefined images by means of face recognition module. The proposed system is achieved an accuracy of 93% to 98% for face recognition.
Project Objectives- To develop SMART ATTENDANCE that is reliable, practical, and eliminates disturbance, deception and time wastes in attendance.
- To develop a system that will get the performance of the students without any deception.
Main components of the system and tools and techniques used for the system to be fully implemented. Actually, this system is a desktop application and its goal is attendance marking and invigilation of students. Our system uses data-sets of students and deep learning/machine learning algorithms to achieve its intended purpose.
System Architecture
In system architecture, high level representation of the structure of our system will be discussed. Our system has two main objectives and revolves around five main components. Objectives are attendance marking and invigilation of students and main components are Image Acquisition, Facial detection, Feature extraction, Facial recognition and Body movement detection. Our system is a desktop application, once installed, it will do most of the work by itself. First data of the students will be gathered and store into an SQL database.
Tools and Technology
Python: It is powerful programming language which follow object oriented methodology. It helps in code clarity and utilize white spaces by composing logical code for small problems as well as big critical problems.
Keras: Keras is an API in neural networking. It is user friendly, easily extensible and mostly importantly it works well with python.
Tensor flow: It is a library in artificial intelligence that is used for the creation of models in large scale neural networking. It also used for making predictions, understanding, discovering etc.
Dlib library: In computer vision Dlib library is commonly practice to approximate the location on 68 coordinates e,g X.Y. This contains pre-trained models that act as facial detector and map the points on individuals face.
System Flow:
In the development of this application we will use incremental model because requirements may changes on the stakeholders end. So the whole system is divided into four phases
Phase 1:
In first phase we will develop desktop application through which we gather the data of the students, prepare data sets and train the model.
Phase 2:
In this phase we will be working on the attendance module and checking the trained model on the data-set.
Phase 3:
In this phase we will be working on detection of body movements and making sure that the intended body parts movement must be detected and recognized with greater accuracy
Phase 4:
We will integrate all the modules and run it by desktop application and test the application as a whole.
Benefits of the Project- Reduce paperwork and save time and money.
- Improve visibility to track and manage student attendance & absenteeism across multiple campuses
- Easy attendance recording using facial recognition.
- Track the attendance of teachers and staff, assign work and manage allocation
SYSTEM REQUIREMENTS and SOFTWARE INTERFACES
Hardware Requirements
• Core i7 (4th generation quad core,16gb Ram or more,1 TB Hard Disk or equivalent )
• GPU (minimum 2 GB)
• High Definition Camera
Software Requirements
• Python 3.5 or latest version
• Wampserver/MySqlserver
• Spyder
• Windows 8 or higher
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 2000 | |||
| Web camera | Equipment | 1 | 2000 | 2000 |