Attendence-Management-system using face recognition
Attendence Management System using Face Recognition as a part of Collage Mini Project
How to run the project:
• Download or clone the Repository to your device.
• Open the command prompt and type "pip install -r requirements.txt" to install the required packages for the project.
• create folders of named as TrainingImage and Attendence respectively.
• Open "attendance.py" and "automaticAttendance.py" and change all the path according to your system.
• Create the folders for subjects we require to take attendance in the "Attendence" folder.
• Run "attendance.py" file to register your face so that the system can identify you.
The project workflow can be described as follows:
• Click on "Register New Student" and a small window will pop up where you have to enter your ID and name. Then click on the "Take Image" button to capture your face. A camera window will pop up and take up to 100 images (you can change the number of images it can take) and store them in the "TrainingImage" folder.
• Click on the "Train Image" button to train the model and convert all the images into a numeric format so that the computer can understand them. The system will take some time to complete the training, depending on your system.
• After training the model, click on "Automatic Attendance". Enter the subject name, and the system will fill in attendance by recognizing your face using the trained model. It will create a separate .csv file for every subject you enter.
• You can view the attendance record in tabular format by clicking on the "View Attendance" button.
Requirements of the system:
• Python 3.6+
• Ram minimum : 8gb
• 4gb Graphic Card
• your Operating System should Support the required modules.
- Opencv (pip install opencv-python)
- Tkinter (Available in python)
- PIL (pip install Pillow)
- Pandas. (pip install pandas)
- Numpy. (pip install numpy)
- Pillow (pip install Pillow)
• haarcascade_frontalface_default.xml
- The folder "haarcascades" contains pre-trained classifiers for detecting specific types of objects such as faces (frontal and profile) and pedestrians. Some of the classifiers may have unique licenses that require further investigation. https://github.com/opencv/opencv/tree/master/data/haarcascades
• The quality of images is crucial as it can impact the accuracy of the system by introducing noise.
For any more explaination a pdf of report f my project is attached along with project files(Facerecognitiom_Report.pdf).
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Basic Layyout or GUI of the project:
