upendra14 / attendance-sculptor

Attendance System using Face Recognition (HOG)

Attendance-System Using Face-Recognition in Real-Time

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

Online Version:
you can try it from here

Offline Version:
Follow these steps to try it:

1. Clone this repo to get all the code and pre-trained model(pickle_file).
2. Change current directory into the cloned repo folder.
3. Install all of the libraries from environment.yml file by using these commands.

conda env create -f environment.yml
conda activate attendance

(optional step) to check if all libraries installed

conda env list

4. Install all of the dependencies from packages.txt using this command.

• Linux users: cmake is a must.

sudo apt-get install cmake libgtk-3-dev freeglut3-dev

• Windows users:
  You need to install visual studio community version from here and make sure that cmake is checked when installing because it is a must.

5. Run this script streamlit_local_app_bussines_ready.py to try the offline version by running this code in the cloned repo directory after installing all dependencies.

streamlit run streamlit_local_app_bussines_ready.py

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 script, and put it on Training mode, and 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 link.

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.