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Fingerprint Presentation Attack Detection by Region Decomposition

delete2024-01-01
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PRE
AI
H
Hongyan Fei
黄
黄传威 (Chuanwei Huang)
S
Song Wu
Z
Zheng Wang
Z
Zexi Jia
J
Jufu Feng *
DOI:10.1109/TIFS.2024.3372813delete
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摘要

摘要

En 中文
Fingerprint Presentation Attack Detection (PAD) is a crucial step in automatic fingerprint identification systems, which safeguards users from unauthorized malicious access. However, current presentation attack (i.e. spoof) techniques can forge intricate details of fingerprints (such as sweat holes), which makes the artifact evidence harder to detect. In this paper, we propose a novel PAD method from the perspective of decomposition to highlight the artifact evidence in each constituent element. Specifically, we utilize the fingerprint enhancement to decompose the fingerprint into the ridge region and the edge region. We observe that artifact evidence mainly exists in the gradient field within the ridge region, while it primarily resides in the spatial domain within the edge region. Then we propose an Orientation-Based Central Difference Convolution (OB-CDC) layer to prioritize gradient variations along the ridge direction. To further enhance robustness, we propose a Minutia Patches Random Rotation (MPRR) operation to disrupt the identity information of the fingerprint while preserving the artifact evidence. By integrating these techniques, we propose a two-stream network called Presentation-Attack-Detection-with-Region-Decomposition-Network (PADRD-Net) which integrates the processed feature of the ridge region and the edge region through a halfway fusion ResNet-18 structure. Experimental results on the LivDet 2021 dataset show that our proposed PADRD-Net can achieve 20.39% on BPCER@APCER = 1% and 87.12% on TDR@FDR = 1%, significantly outperforms the state-of-the-art. We also achieve outstanding performance in both the cross-sensor scenario and the cross-sensor and cross-material scenario. Extensive ablation studies and analysis experiments further indicate the effectiveness and robustness of our method.
Keyword:
Fingerprint recognition
Feature extraction
Convolution
Image edge detection
Task analysis
Faces
Estimation
Fingerprint
presentation attack detection
decomposition
multi-domain
OB-CDC layer
minutia patches random rotation

期刊

IEEE Transactions on Information Forensics and Security 封面图
IEEE Transactions on Information Forensics and Security
IF:
8
论文数:
5.3K
被引数:
2.3W

机构

P
peking university
学者数:
11.9W
论文数: 8.7W
被引数: 146
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