Hyp-OC Model Card

Project Page | Paper (ArXiv) | Code

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

@article{narayan2024hyp,
  title={Hyp-OC: Hyperbolic One Class Classification for Face Anti-Spoofing},
  author={Narayan, Kartik and Patel, Vishal M},
  journal={arXiv preprint arXiv:2404.14406},
  year={2024}
}

Please check our GitHub repository for complete instructions.