| | --- |
| | 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} |
| | } |
| | ``` |
