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--- |
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license: mit |
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language: |
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- en |
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--- |
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# Hyp-OC Model Card |
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<div align="center"> |
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[**Project Page**](https://kartik-3004.github.io/hyp-oc/) **|** [**Paper (ArXiv)**](https://arxiv.org/pdf/2404.14406.pdf) **|** [**Code**](https://github.com/Kartik-3004/hyp-oc) |
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</div> |
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## Introduction |
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Hyp-OC, is the first work exploring hyperbolic embeddings for one-class face anti-spoofing (OC-FAS). |
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We show that using hyperbolic space helps learn a better decision boundary than the Euclidean counterpart, |
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boosting one-class face anti-spoofing performance. |
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<div align="center"> |
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<img src='assets/visual_abstract.png' height="50%" width="50%"> |
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</div> |
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## Training Framework |
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<div align="center"> |
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<img src='assets/framework.png'> |
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</div> |
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Overview of the proposed pipeline: Hyp-OC. The encoder extracts facial features which are used to estimate the mean of Gaussian |
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distribution utilized to sample pseudo-negative points. The real features and pseudo-negative features are then concatenated |
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and passed to FCNN for dimensionality reduction. The low-dimension features are mapped to Poincaré Ball using *exponential map*. |
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The training objective is to minimize the summation of the proposed loss functions Hyp-PC} and Hyp-CE. The result is a separating |
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*gyroplane* beneficial for one-class face anti-spoofing. |
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## Usage |
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The pre-trained weights can be downloaded directly from this repository or using python: |
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```python |
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from huggingface_hub import hf_hub_download |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="pretrained_weights/vgg_face_dag.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/CASIA_MFSD/casia_mfsd/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/OULU_NPU/oulu_npu/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/ROSEYoutu/rose_youtu/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/ReplayAttack/replayattack/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/ICM/icm/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/OCI/oci/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/OCM/ocm/best_epoch.pth", local_dir="./") |
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hf_hub_download(repo_id="kartiknarayan/hyp-oc", filename="weights/OMI/omi/best_epoch.pth", local_dir="./") |
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``` |
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## Citation |
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```bibtex |
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@inproceedings{narayan2024hyp, |
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title={Hyp-oc: Hyperbolic one class classification for face anti-spoofing}, |
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author={Narayan, Kartik and Patel, Vishal M}, |
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booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)}, |
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pages={1--10}, |
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year={2024}, |
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organization={IEEE} |
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} |
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``` |
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Please check our [GitHub repository](https://github.com/Kartik-3004/hyp-oc) for complete instructions. |
