Face Verification System

Setup

Create a Conda Environment

Run the following command to create a new Conda environment named tf2:

conda create --name tf2 python==3.9.21

Install Requirements

Use the provided requirements.txt file to install dependencies:

pip install -r requirements.txt

Note: If you encounter any missing modules while running the project, install them using pip and update the requirements.txt file accordingly.

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Project Structure

Data Pipeline:

1. app.py

• This is the main file to run the streamlit app.
• It captures the image using the webcam.
• crops the face using Face Detection.
• saves the image in a MongoDB database.

streamlit run app.py

2. crop_image.py

• This file contains the function to crop the face from the image.

The model

1. verification.ipynb

• This file contains the code to train the verification model.

2. recognition.ipynb

• This file contains the code to train the recognition model.

Future Improvements

2. Implement a more robust database solution.
3. Train and integrate a face verification model.
4. Explore data augmentation techniques for improved model performance.