---
license: mit
language:
- en
---

# Hyp-OC Model Card

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[**Project Page**]() **|** [**Paper (ArXiv)**]() **|** [**Code**]()


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## Introduction

Hyp-OC, is the first work exploring hyperbolic embeddings for one-class face anti-spoofing (OC-FAS). 
We show that using hyperbolic space helps learn a better decision boundary than the Euclidean counterpart, 
boosting one-class face anti-spoofing performance.

<div  align="center">
<img src='assets/intro_viz.png'>
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## Training Framework

Overview of the proposed pipeline: Hyp-OC. The encoder extracts facial features which are used to estimate the mean of Gaussian 
distribution utilized to sample pseudo-negative points. The real features and pseudo-negative features are then concatenated 
and passed to FCNN for dimensionality reduction. The low-dimension features are mapped to Poincaré Ball using *exponential map*. 
The training objective is to minimize the summation of the proposed loss functions Hyp-PC} and Hyp-CE. The result is a separating 
*gyroplane* beneficial for one-class face anti-spoofing.

<div  align="center">
<img src='assets/main_archi.png'>
</div>

## Usage

The pre-trained weights can be downloaded directly from this repository or using python:
```python
from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="pretrained_weights/vgg_face_dag.pth", local_dir="./")
```

## Citation
```bibtex
Coming soon ...
```

Please check our [GitHub repository]() for complete instructions.