moca-face-matcher 0.2.1

Ultra-fast face matching (OpenCV YuNet + SFace + FAISS).

moca-face-matcher

Ultra-fast face matching library using OpenCV's YuNet for detection, SFace for recognition, and FAISS for high-performance vector search. Designed as both a production-grade SDK and a powerful CLI tool.

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Features

• Blazing Fast: Integrated with FAISS for near-instant search in large databases.
• State-of-the-Art Models: Uses YuNet (fastest detection) and SFace (robust recognition).
• GPU Ready: Hardware acceleration support via OpenCV DNN/CUDA.
• Developer First: Clean SDK interface with strict type hinting (Mypy) and JSON-based secure cache.
• Secure by Design: Automatic SHA-256 model verification and no insecure serialization (no pickle).
• Portable Cache: Uses relative paths in the JSON cache, making it portable across different machines.

Requirements

• Python 3.9 or superior.
• Internet connection for the first run (downloads ~40 MB of ONNX models from OpenCV Zoo).

Installation

pip install moca-face-matcher

For local development:

git clone https://github.com/Moca9801/face-match.git
cd face-match
pip install -e ".[dev]"

Usage

Command Line Interface (CLI)

Search for a face in a gallery folder:

face-match path/to/query.jpg --db path/to/gallery/

Library Usage (SDK)

The core function is find_matches. It returns a structured dictionary and does not print to the console, making it ideal for integration into other systems.

from pathlib import Path
from face_match import find_matches

results = find_matches(
    query=Path("query.jpg"),
    db=Path("./gallery"),
    top=3,
    threshold=0.4
)

print(f"Scanned {results['total_scanned']} images.")
for match in results['results']:
    print(f"Match found: {match['path']} (Distance: {match['distance']})")

CLI vs SDK Mapping

CLI Option	SDK Parameter (find_matches)	Description
query	query (Path)	Path to the query image.
--db	db (Path)	Path to the gallery/database folder.
--top	top (int)	Number of results to return.
--metric	distance (int)	0 for Cosine (default), 1 for L2.
--threshold	threshold (float)	Similarity threshold.
--device	device (str)	cpu (default) or gpu.
--rebuild	rebuild_cache (bool)	Force rescan of all images.

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Technical Details

Metrics and Thresholds

The library uses predefined thresholds based on the SFace model's official recommendations:

• Cosine Similarity (--metric coseno): Matches range from -1 to 1. Higher is better. Recommended threshold: 0.363.
• L2 Distance (--metric l2): Matches start from 0. Lower is better. Recommended threshold: 1.128.

Development and Testing

Contributors are welcome! To run quality checks locally:

# Run Linting
ruff check src tests

# Run Type Checking
mypy src/face_match

# Run Tests
pytest

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Security and Data Privacy

[!CAUTION] Biometric Data on Disk: The .face_embeddings_cache.json file generated in your database folder contains mathematical representations (embeddings) and relative file paths.

• Do not share this file.
• Treat it as sensitive personal data.
• Access to the file should be restricted using system-level permissions.

Disclaimer

• This software is not a certified biometric identification system.
• It is intended for research and low-risk identification tasks.
• The author is not responsible for misuse or legal implications of using biometric data.

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Versión en Español (Resumen)

moca-face-matcher es una librería de búsqueda de coincidencias faciales ultra rápida.

Instalación rápida

pip install moca-face-matcher

Características

• Búsqueda vectorial con FAISS.
• Modelos YuNet + SFace.
• Soporte para GPU.
• Caché segura en JSON con rutas relativas.

Seguridad

Este software maneja datos biométricos. El archivo de caché .face_embeddings_cache.json es sensible y no debe ser compartido.

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License

Distributed under the MIT License. See LICENSE for more information.