# 3D Face Anti-spoofing with Factorized Bilinear Coding

arXiv: https://arxiv.org/abs/2005.06514 · HTML: https://mlsanity.com/paper/2005.06514/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 20 of 20)

- [Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing](https://mlsanity.com/paper/2203.05340/) (2022)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.06148/) (2021)
- [GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection](https://mlsanity.com/paper/2211.08615/) (2022)
- [Fairness in Face Presentation Attack Detection](https://mlsanity.com/paper/2209.09035/) (2022)
- [Surveillance Face Anti-spoofing](https://mlsanity.com/paper/2301.00975/) (2023)
- [S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens](https://mlsanity.com/paper/2309.04038/) (2023)
- [A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking](https://mlsanity.com/paper/2105.02480/) (2021)
- [Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach](https://mlsanity.com/paper/2108.08920/) (2021)
- [Domain Generalization via Ensemble Stacking for Face Presentation Attack Detection](https://mlsanity.com/paper/2301.02145/) (2023)
- [Meta Generative Attack on Person Reidentification](https://mlsanity.com/paper/2301.06286/) (2023)
- [Surveillance Face Presentation Attack Detection Challenge](https://mlsanity.com/paper/2304.07580/) (2023)
- [Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion](https://mlsanity.com/paper/2108.08301/) (2021)
- [Generalized Face Liveness Detection via De-fake Face Generator](https://mlsanity.com/paper/2401.09006/) (2024)
- [Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels](https://mlsanity.com/paper/2204.10675/) (2022)
- [Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results](https://mlsanity.com/paper/2304.05753/) (2023)
- [3D Face Recognition: A Survey](https://mlsanity.com/paper/2108.11082/) (2021)
- [EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing](https://mlsanity.com/paper/2302.08674/) (2023)
- [Face Presentation Attack Detection](https://mlsanity.com/paper/2212.03680/) (2022)
- [A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection](https://mlsanity.com/paper/2212.06958/) (2022)

## Abstract (arXiv)

We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks, and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of multiple color channels (namely MCFBC), targets at learning subtle fine-grained differences between real and fake images. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection. A large-scale wax figure face database (WFFD) with both images and videos has also been collected as super-realistic attacks to facilitate the study of 3D face presentation attack detection. Extensive experimental results show that our proposed method achieves the state-of-the-art performance on both our own WFFD and other face spoofing databases under various intra-database and inter-database testing scenarios.