The primary goal of this project is to create an automated student attendance system based on facial recognition. The test pictures and training images of this suggested technique are restricted to frontal and upright facial images that only contain a single face to improve performance. To ensure no quality variation, the test photographs and training images must be taken using the same equipment. To be identified, the pupils must also register in the database. The user-friendly interface allows for immediate enrollment.
Background
This report discusses the importance of face recognition in daily life and how it is achieved through human intellect and the analysis of visual information. However, humans have limitations in their ability to recognize faces, which is why computers with almost limitless memory, high processing speed, and power are used in face recognition systems. The report also highlights the widespread use of facial recognition technology in various fields such as airport security, criminal investigations, and social networking sites. Additionally, the report provides a brief history of face recognition research starting from the 1960s, including the development of methods such as the detection of facial features and principle component analysis.
Problem Statement
The traditional method of recording student attendance has several issues and can be replaced with a facial recognition-based system that is simple and efficient. The system can overcome the problem of fraudulent approaches and eliminate the need for manual attendance taking. However, the training procedure for the system is time-consuming, and variations in illumination and head postures can impair its effectiveness. Therefore, a real-time functioning system with high precision and quick computation times must be developed.
Aims and Objectives
The goal of this project is to create an automated student attendance system based on facial recognition. The following are expected results to meet the objectives: To identify the facial segment in the video frame. To identify the useful aspects on the observed face. To categorize the traits in order to identify the observed face. To note the indicated student's attendance
Literature Survey
Traditional methods of taking attendance in classrooms can be time-consuming, disruptive, and prone to errors. A facial recognition-based attendance system offers a simpler and more accurate solution. By using cameras to scan the faces of students as they enter the classroom, the system can quickly and easily mark attendance without the need for manual processes.
However, implementing a facial recognition attendance system comes with its own set of challenges. One of the main challenges is ensuring that the system can accurately identify individuals, even in varying lighting conditions or with different facial expressions. This requires a complex algorithm that can analyze facial features and compare them to a database of known faces.
To overcome these challenges, researchers have been working on developing more advanced facial recognition algorithms that can account for these variations and provide higher levels of accuracy. In addition, real-time functioning attendance systems must be created to identify students within specific time limits and avoid missing any students.
Overall, facial recognition-based attendance systems have the potential to greatly improve the efficiency and accuracy of attendance tracking in classrooms. With continued research and development, they can become even more effective and widespread in educational institutions.
Code and Explanation
Step 1: Data Collection and Preprocessing
The first step is to collect face images of the individuals who will be enrolled in the system. This can be done using a camera or other imaging device. The images should be preprocessed to ensure that they are of a consistent size and quality. You can use image processing libraries such as OpenCV or PIL to accomplish this.
Step 2: Face Detection
Once you have collected the images, the next step is to detect the faces within the images. The goal is to accurately identify the location of the face within the image. Here , we used the Haar Cascade Model for face detection
Step 3: Face Recognition
After detecting the face, the next step is to recognize it. You can use pre-trained face recognition models or train your own model using machine learning algorithms such as Support Vector Machines or Neural Networks. The goal is to match the detected face with a known face from the system’s database via LBPH Algorithm.
Step 4: Attendance Tracking
Once a face has been recognized, the system can track attendance by recording the time and date of the recognition event. This can be accomplished using a simple database or a cloud-based service.
Step 5: User Interface
To make the system easy to use, you can create a user interface that displays the attendance records and allows administrators to manage the system. The Tkinter UI makes it easy for users to view attendance records and perform other tasks such as adding new users to the system.
Result
After evaluating the performance of our model, if we observe that it is performing well on the training and validation data, we can confidently use this model to predict outcomes on new and unseen data. In other words, we can use this model for marking attendance in various fields.