pos_face_recognition/ai_face_recognition_server/README.md

AI Face Recognition Server

A Flask-based standalone server for face recognition, designed for integration with Odoo Point of Sale (POS).

Features

• Real-time Recognition: Processes images and returns matches with metadata (ID, Name, Probability).
• Face Training: API endpoint to register new faces from base64 images.
• Production Ready: Supports Gunicorn for stability and concurrency.
• Optimized for POS: Default 60% match threshold handles varied lighting conditions well.

Prerequisites

• Python 3.10+
• System libraries for dlib (cmake, g++, etc.)
• Virtual environment (recommended)

Installation

1. Activate the Virtual Environment:

   source .venv/bin/activate
   

2. Install Dependencies:

   pip install -r requirements.txt
   

Running the Server

Production (Recommended)

Run using Gunicorn for better performance and stability:

.venv/bin/gunicorn -w 1 -b 0.0.0.0:5000 app:app --timeout 120

Development

python app.py

API Endpoints

1. Recognize Face

• Endpoint: POST /recognize
• Body: { "image": "base64_string_without_header" }
• Response: List of matches with id, name, and probability.

2. Train Face

• Endpoint: POST /train
• Body: { "partner_id": 123, "name": "John Doe", "images": ["base64_1", "base64_2"] }
• Description: Saves images to the faces/ directory and updates the in-memory model.

Matching Logic

• Tolerance: 0.4 (Distance)
• Probability: 1.0 - distance (Matches >= 60% are returned)
• Diagnostics: Server logs the exact distance and probability to the terminal for every request.

Directory Structure

• app.py: Main Flask application.
• faces/: Directory where trained face images are stored.
• requirements.txt: Python dependencies.