返回
High resolution partial fingerprint alignment using pore-valley descriptors
DOI:10.1016/j.patcog.2009.08.004.png)
摘要
En 中文
This paper discusses the alignment of high resolution partial fingerprints, which is a crucial step in partial fingerprint recognition. The previously developed fingerprint alignment methods, including minutia-based and non-minutia feature based ones, are unsuitable for partial fingerprints because small fingerprint fragments often do not have enough features required by these methods. In this paper, we propose a new approach to aligning high resolution partial fingerprints based on pores, a type of fingerprint fine ridge features that are abundant on even small fingerprint areas. Pores are first extracted from the fingerprint images by using a difference of Gaussian filtering approach. After pore detection, a novel pore-valley descriptor (PVD) is proposed to characterize pores based on their locations and orientations, as well as the ridge orientation fields and valley structures around them. A PVD-based coarse-to-fine pore matching algorithm is then developed to locate pore correspondences. Once the corresponding pores are determined, the alignment transformation between two partial fingerprints can be estimated. The proposed method is compared with representative minutia based and orientation field based methods using the established high resolution partial fingerprint dataset and two fingerprint matchers. The experimental results show that the PVD-based method can more accurately locate corresponding feature points, estimate the alignment transformations, and hence significantly improve the accuracy of high resolution partial fingerprint recognition. (C) 2009 Elsevier Ltd. All rights reserved.
Keyword:
Fingerprint alignment
Partial fingerprints
High resolution fingerprints
Pores
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes
PATTERN RECOGNITION
IF7.6
Predicting arrhythmic events after acute myocardial infarction using the signal-averaged electrocardiogram使用信号平均心电图预测急性MI后的心律失常事件

