license: mit
language:
- en
Hyp-OC Model Card
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.
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.
Usage
The pre-trained weights can be downloaded directly from this repository or using 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
@inproceedings{narayan2024hyp,
title={Hyp-oc: Hyperbolic one class classification for face anti-spoofing},
author={Narayan, Kartik and Patel, Vishal M},
booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)},
pages={1--10},
year={2024},
organization={IEEE}
}
Please check our GitHub repository for complete instructions.