Face-Recognition-Attendance-System-main.zip

# Face-Recognition-Attendance-Management-System Attendance Management System based on Face Recognition using Python and OpenCv ### Sourcerer <img src="https://avatars.githubusercontent.com/u/84435079?v=4" height="50px" width="50px" alt=""/> ### Code Requirements - Opencv(`pip install opencv-python`) - Tkinter(Available in python) - PIL (`pip install Pillow`) - Pandas(`pip install pandas`) ### What steps you have to follow?? - Download my Repository - Create a `TrainingImage` folder in a project. - Open a `AMS_Run.py` and change the all paths with your system path - Run `AMS_Run.py`. ### Project Structure - After run you need to give your face data to system so enter your ID and name in box than click on `Take Images` button. - It will collect 200 images of your faces, it save a images in `TrainingImage` folder - After that we need to train a model(for train a model click on `Train Image` button. - It will take 5-10 minutes for training(for 10 person data). - After training click on `Automatic Attendance` ,it can fill attendance by your face using our trained model (model will save in `TrainingImageLabel` ) - it will create `.csv` file of attendance according to time & subject. - You can store data in database (install wampserver),change the DB name according to your in `AMS_Run.py`. - `Manually Fill Attendance` Button in UI is for fill a manually attendance (without facce recognition),it's also create a `.csv` and store in a database. ### Screenshots ### Basic UI <img src="https://github.com/Pragya9ps/Face-Recognition-Attendance-System/blob/main/Screenshot%20(31).png"> ### When it Recognises me <img src="https://github.com/Pragya9ps/Face-Recognition-Attendance-System/blob/main/Screenshot%20(33).png"> ### While filling automatic attendance <img src="https://github.com/Pragya9ps/Face-Recognition-Attendance-System/blob/main/Screenshot%20(38).png"> ### Manually attendance filling UI <img src="https://github.com/Pragya9ps/Face-Recognition-Attendance-System/blob/main/Screenshot%20(35).png"> ### Video demo [Youtube](https://youtu.be/onms2KDOTtY) ### Notes - It will require high processing power(I have 8 GB RAM) - Noisy image can reduce the accuracy, so quality of images should be good.