Face Recognition & Anti-Spoofing Models

This repository provides scripts and tools for converting, optimizing, and testing face recognition and anti-spoofing models. It includes ONNX model exports, quantization utilities, and Python scripts for handling various model architectures such as FaceNet, RetinaFace, and MiniFASNet.

It also comes with a complete TypeScript implementation for face matching, enabling fast and flexible inference in JavaScript environments.

Repository Structure

• exported_models/ — Contains ONNX and quantized models for FaceNet, RetinaFace, and MiniFASNet variants.
• python_scripts/ — Python scripts for model conversion, optimization, quantization, and backbone definitions.
• python_scripts/keras_layers/ — Custom Keras layers.
• source_weights/ — Original model weights in PyTorch and Keras h5 formats.
• ts_src/ — TypeScript source files for face matching, image utilities, and model inference.
• test_photos/ — Sample images for testing model inference and annotation.
• test_results/ — Output images from model inference and annotation scripts.

Setup

Setup (TypeScript & Python)

First, install both Node.js and Python dependencies:

npm install

This will automatically install Python dependencies using a postinstall script defined in package.json.

Then, download the required model weights:

npm run download-weights

Usage

Model Conversion & Quantization

To convert models, use the npm script:

npm run convert-models

This will run the necessary Python scripts for model conversion automatically.

To quantize ONNX models, use the npm script:

npm run quantize-models

Running TypeScript Inference

• Use files in ts_src/ for face matching and anti-spoofing inference.

To run face matching tests, use the npm script:

npm run test-face-match

This will execute the TypeScript test script for face matching.

Testing

• Test images are in test_photos/.
• Results are saved in test_results/.

License

This repository is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

This repository uses models and weights from the following sources:

• FaceNet and RetinaFace weights: https://github.com/serengil/deepface_models
• MiniFASNet (Anti-Spoofing): https://github.com/minivision-ai/Silent-Face-Anti-Spoofing

Please refer to the respective repositories for their original licenses and citations.