README.md

---
pipeline_tag: image-feature-extraction
---

# MeFEm: Medical Face Embedding model

MeFEm is a vision model based on a modified Joint Embedding Predictive Architecture (JEPA) for biometric and medical analysis from facial images.

## Model Description

MeFEm introduces several modifications to the JEPA framework to optimize for medical and biometric facial analysis:
- **Axial stripe masking strategy**: Focuses learning on semantically relevant regions of the face.
- **Circular loss weighting scheme**: A novel weighting approach for the training objective.
- **Probabilistic reassignment of the CLS token**: Designed to improve the quality of linear probing for downstream tasks.

The model was trained on a consolidated dataset of curated images and outperforms strong baselines like FaRL and Franca on core anthropometric tasks and Body Mass Index (BMI) estimation, despite using significantly less data.

## Resources

- **Paper**: [MeFEm: Medical Face Embedding model](https://huggingface.co/papers/2602.14672)

## Citation

```bibtex
@article{mefem2026,
  title={MeFEm: Medical Face Embedding model},
  author={},
  journal={arXiv preprint arXiv:2602.14672},
  year={2026}
}
```