rezajahadi / airport-authentication

Advanced Face Detection System using deep learning for passenger identification based on stored facial embeddings.

Face Detection App

This project uses image processing and deep learning to detect faces in passengers' photos and compare them against a database of stored photos.

Overview

The app loads a deep learning model trained to detect faces in images. When a new passenger photo is input, the model detects any faces and extracts facial embeddings. These embeddings are compared against stored embeddings for known passengers using cosine similarity. If a match is found, the passenger is identified.

Usage

1. Ensure you have Python 3 and the requirements installed.

2. Download the pre-trained face detection model from Google Drive and place in the trainer folder.

3. Run python app.py to start the app.

4. Input a passenger photo file path when prompted.

5. The app will detect any faces, extract embeddings, compare against known passengers in passengers.db and output the identified passenger name if a match is found.

Files

• app.py - Main application logic
• detect.py - Functions for face detection and embedding
• database.py - Passenger database interface
• passengers.db - SQLite database containing passenger names and facial embeddings
• trainer/ - Folder containing pre-trained face detection model files

Requirements

• Python 3
• OpenCV
• SQLite3
• Tensorflow
• See requirements.txt for full requirements

Credits

The face detection model is a pre-trained ResNet model from Anthropic. The database stores facial embeddings extracted using this model.

Let me know if you would like me to modify or expand this README!