# PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing

arXiv: https://arxiv.org/abs/2004.11744 · HTML: https://mlsanity.com/paper/2004.11744/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Review of Face Presentation Attack Detection Competitions](https://mlsanity.com/paper/2112.11290/) (2021)
- [Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection](https://mlsanity.com/paper/2505.03610/) (2025)
- [Hyperbolic Face Anti-Spoofing](https://mlsanity.com/paper/2308.09107/) (2023)
- [Advancing Cross-Domain Generalizability in Face Anti-Spoofing: Insights, Design, and Metrics](https://mlsanity.com/paper/2406.12258/) (2024)
- [Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradation](https://mlsanity.com/paper/2509.23475/) (2025)
- [Domain-generalizable Face Anti-Spoofing with Patch-based Multi-tasking and Artifact Pattern Conversion](https://mlsanity.com/paper/2604.09018/) (2026)

## Abstract (arXiv)

Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. Addressing the shortage of multi-modal face dataset, CASIA recently released the largest up-to-date CASIA-SURF Cross-ethnicity Face Anti-spoofing(CeFA) dataset, covering 3 ethnicities, 3 modalities, 1607 subjects, and 2D plus 3D attack types in four protocols, and focusing on the challenge of improving the generalization capability of face anti-spoofing in cross-ethnicity and multi-modal continuous data. In this paper, we propose a novel pipeline-based multi-stream CNN architecture called PipeNet for multi-modal face anti-spoofing. Unlike previous works, Selective Modal Pipeline (SMP) is designed to enable a customized pipeline for each data modality to take full advantage of multi-modal data. Limited Frame Vote (LFV) is designed to ensure stable and accurate prediction for video classification. The proposed method wins the third place in the final ranking of Chalearn Multi-modal Cross-ethnicity Face Anti-spoofing Recognition Challenge@CVPR2020. Our final submission achieves the Average Classification Error Rate (ACER) of 2.21 with Standard Deviation of 1.26 on the test set.