cosanlab/py-feat

Facial Expression Analysis Toolbox

What it solves

Py-FEAT provides a comprehensive toolkit for facial expression (FEX) research, simplifying the process of extracting emotional and physical facial data from images and videos.

How it works

The library uses pretrained models (automatically downloaded from HuggingFace) to detect faces and extract specific facial features, including:

  • Emotional expressions (e.g., happiness, sadness, anger).
  • Facial muscle movements (known as action units).
  • Facial landmarks.

It supports hardware acceleration on CPU, NVIDIA GPUs (CUDA), and Apple Silicon (MPS).

Who it’s for

Researchers and developers working in facial expression analysis, psychology, and computer vision.

Highlights

  • Automated weight management: Model weights are downloaded automatically as needed.
  • Cross-platform hardware support: Compatible with CUDA and Metal (MPS) for faster processing.
  • End-to-end pipeline: Includes tools for detection, extraction, preprocessing, analysis, and visualization of facial data.

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