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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!