Face_Recognition_Attendance_system

This model uses a combination of Haar cascade for face detection, Face Recognition API for face recognition, and KNN (K-Nearest Neighbor) for classification.

Dependencies

• Python 3
• Haar cascade
• Face Recognition API
• KNN (K-Nearest Neighbor)
• numpy
• opencv

Model Training

1. Preprocess the input data using Haar cascade and Face Recognition API.
2. Train the KNN (K-Nearest Neighbor) model using the preprocessed data.

Installation

For Linux/MacOS/Windows:

• Clone the repo: git clone https://github.com/rishav-dahal/Attendance_system_KNN.git
• Make a virtual environment inside server dir. Eg: python -m venv venv
• Activate the virtual environment. Eg: source ./venv/bin/activate
• Install the dependencies from the repo's requirements.txt file. pip install -r requirements.txt
• Run the following code: python train.py to train
• Run the server. python app.py
• Go to the url localhost:8000 on your browser.