# Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

arXiv: https://arxiv.org/abs/2111.07620 · HTML: https://mlsanity.com/paper/2111.07620/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Single-Side Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2004.14043/) (2020)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [Deep Representations for Iris, Face, and Fingerprint Spoofing Detection](https://mlsanity.com/paper/1410.1980/) (2014)
- [Biometric Presentation Attack Detection: Beyond the Visible Spectrum](https://mlsanity.com/paper/1902.11065/) (2019)
- [LivDet 2017 Fingerprint Liveness Detection Competition 2017](https://mlsanity.com/paper/1803.05210/) (2018)
- [Group-wise Inhibition based Feature Regularization for Robust Classification](https://mlsanity.com/paper/2103.02152/) (2021)
- [Universal Material Translator: Towards Spoof Fingerprint Generalization](https://mlsanity.com/paper/1912.03737/) (2019)
- [RaspiReader: Open Source Fingerprint Reader](https://mlsanity.com/paper/1712.09392/) (2017)
- [Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015](https://mlsanity.com/paper/1609.01648/) (2016)
- [OCT Fingerprints: Resilience to Presentation Attacks](https://mlsanity.com/paper/1908.00102/) (2019)
- [Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns](https://mlsanity.com/paper/1508.00537/) (2015)
- [Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations](https://mlsanity.com/paper/1705.07972/) (2017)

## Built on by (most cited first, 5 of 5)

- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing](https://mlsanity.com/paper/2401.01102/) (2024)
- [Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage](https://mlsanity.com/paper/2504.11066/) (2025)
- [Internal Structure Attention Network for Fingerprint Presentation Attack Detection from Optical Coherence Tomography](https://mlsanity.com/paper/2303.11034/) (2023)
- [Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2607.24090/) (2026)

## Abstract (arXiv)

Due to the diversity of attack materials, fingerprint recognition systems (AFRSs) are vulnerable to malicious attacks. It is thus important to propose effective fingerprint presentation attack detection (PAD) methods for the safety and reliability of AFRSs. However, current PAD methods often exhibit poor robustness under new attack types settings. This paper thus proposes a novel channel-wise feature denoising fingerprint PAD (CFD-PAD) method by handling the redundant noise information ignored in previous studies. The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels. Then, the propagation of "noise" channels is suppressed in the feature map to reduce interference. Specifically, a PA-Adaptation loss is designed to constrain the feature distribution to make the feature distribution of live fingerprints more aggregate and that of spoof fingerprints more disperse. Experimental results evaluated on the LivDet 2017 dataset showed that the proposed CFD-PAD can achieve a 2.53% average classification error (ACE) and a 93.83% true detection rate when the false detection rate equals 1.0% (TDR@FDR=1%). Also, the proposed method markedly outperforms the best single-model-based methods in terms of ACE (2.53% vs. 4.56%) and TDR@FDR=1%(93.83% vs. 73.32%), which demonstrates its effectiveness. Although we have achieved a comparable result with the state-of-the-art multiple-model-based methods, there still is an increase in TDR@FDR=1% from 91.19% to 93.83%. In addition, the proposed model is simpler, lighter and more efficient and has achieved a 74.76% reduction in computation time compared with the state-of-the-art multiple-model-based method. The source code is available at https://github.com/kongzhecn/cfd-pad.