Featured Project Section - Attendance Project

What is the attendance project?

This is a project originated at the MSU AI Club trying to achieve a AI related coding project where we could identify faces and hands to track attendance. More information on the github source. Preview on how to install is below! Documentation Website

How to install?

We will be creating a local environment under the project directory, either using venv or conda. Since there are more than one branches in this project, we will have to switch from environment to environment when checking out different branches. So in this tutorial, we have avoided using generic name for our local environment, such as env/ or venv/; please use env_interaction/.

Using pip

Make sure you are not using the pip within conda, otherwise you might encounter an SSLError.

For Windows:

python -m venv .\env_interaction             # create environment
.\env_interaction\Scripts\activate           # activate environment
python -m pip install -r requirements.txt    # install dependencies

For macOS/Unix:

python -m venv ./env_interaction            # create environment
source ./env_interaction/bin/activate       # activate environment
python -m pip install -r requirements.txt   # install dependencies

More about pip: https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/.

Using conda

For Windows:

(gcm conda.exe).Path   # you should see something, meaning that conda is activated
conda env create --prefix .\env_interaction --file environment.yml # create environment
conda activate .\env_interaction                                   # activate environment

For macOS/Unix:

which conda     # you should see something, meaning that conda is activated
conda env create --prefix ./env_interaction --file environment.yml # create environment
conda activate ./env_interaction                                   # activate environment

More about conda: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

How to use?

The face_images file contains images the app will try to recognize. It contains a placeholder picture of Barack Obama. Add a picture of yourself to the face_images file and name it to your name.

Make sure a camera is connected to the computer, then run the following command:

python recognition.py

Currently, only five gestures are supported:

• One (index finger up)
• Two (index finger and middle finger up)
• Three (index finger, middle finger and ring finger up)
• Thumbs up
• Thumbs down

How to train?

This is still under development. For now, we have a very simple model (Support Vector Machine) that trains on ~300 labeled landmarks; see landmarked.csv. The trained model is saved as model.pkl. To (re)train the model, run the following command:

python train.py

What is a landmark?

Landmark, or hand landmark, is a point on the hand that is used for hand gesture recognition. In this project, we use the hand tracking solutions by MediaPipe to detect the 21 landmarks. Then we train our own model to classify the hand gesture using the (x, y, z) coordinates of the landmarks. Below is a figure showing the indices of the landmarks, which are stored in landmark-index.json. Credit: MediaPipe