Attendance-System Using Face-Recognition in Real-Time

Model Demo

Attendance_System.mp4

How To Try This Model

There are two versions (Online and Offline)

	Online	Offline
Training	- Only the host (me) can train new faces.	- When you clone the repo you can train new faces as you want.
Attend from Uploading Photo
Attend from Camera (Photo Mode)
Attend Live

Training :

To train the model on different faces, do the following:

1. Get a photo that contains one person and rename it to the person's name and put it in the db folder like this picture.
   
   
2. Repeat that for as many faces as you want.
3. Finally run the [streamlit_local_app_bussines_ready.py]
4. press the Train The Model button, then you can go for testing.

Testing :

• You have three modes. The best one is Live Attendance (Real case scenario)
  To run it do the following:

1. From the sidebar select Attend Live mode.
2. Select Attendance.csv file, which is a file to record the arrival_time, date, penalty of every attendant.
3. Check run box to start the program then show the camera faces of people you've trained (people in db folder)

• You can also try attending from uploading a picture of your face and it will work as well. To run it do the following:

1. From the sidebar select Attend from uploading image mode.
2. Upload your image or drag & drop it and it will detect your face and make you attend just like this picture. Note: Of course this is for trying purposes the best model is Attend Live described above.

Short Description of the Files

1. [Preparing_local.py]: It has all of the functions necessary for the main offline version script to work.
2. [Preparing_test_online.py]: very similar file to the previous file with only some changes to make the online version work properly.
3. [Training.py]: a script for training the model to memorize faces stored in db folder.
4. [encoded_faces.pickle]: The output file of Training.py script, It contains the encoded features of every face to compare it with new faces.
5. [environment.yml]. and [packages.txt] : libraries and dependencies to make this project work.
6. [streamlit_local_app_bussines_ready.py]: Attend live or from uploading photo or take a photo then attend.
7. [streamlit_test_app_online.py]: very similar file to the previous script but without the Live Attendance Feature and it works online right from this .

How This Model Work (what is going on under the hood)

1. Detecting all of the faces in the picture/video:- using HOG algorithm. This function do the work face_recognition.face_locations.
2. Transform the face to make the eyes and mouth centered after finding main face landmarks using face landmark estimation.
3. Encode the faces by generating 128 different measuremts per face (saving faces). This function do the training (encodings) face_recognition.face_encodings
4. Recognition:- comparing new faces from photo/video with the encoded faces in our dataset. This function face_recognition.compare_faces do the comparing and return a list of True and False.
5. Make the attendance :- markattendance() this function uses OpenCV library to annotate the faces and then add the name each detected face -based on the previous function return face_recognition.compare_faces- to the attendance list [Attendance.csv]

Resources and Note

I have used these resources to build my project:

1. medium article: - It descripe in details how face recognition is really working under the hood.
2. YouTube Tutorial: - for attendance part using OpenCV and HOG. It is basically based on the previous article.
3. I've written the code myself based on the video and enhanced some features like when attending live it was mainly designed to be used for one day only.
   I changed the logic and make it work forever.
   I've aslo added the penalty feature for people who are late to work, (they get a 10$ penalty if they came after 9:00 AM).

To-Do List

• Penalty (10$) for comming late to work (After 9:00 AM).
• Detect 3D faces only. (printed faces or rendered faces on screens shouldn't be detected).