基于四元数主成分分析的多光谱掌纹识别

2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics Pub Date : 2010-08-30 DOI:10.1109/ETCHB.2010.5559287

Xingpeng Xu, Zhenhua Guo

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引用次数: 34

摘要

掌纹在个人识别中有着广泛的应用。为了提高现有掌纹识别系统的性能，提出并设计了多光谱掌纹识别系统。本文提出了一种用四元数表示多光谱掌纹图像并利用四元数主成分分析(QPCA)提取特征的方法，以提高识别性能。采用数据采集装置，在短于15秒的时间内捕获红、绿、蓝和近红外光照下的掌纹图像。采用QPCA对多光谱掌纹图像进行特征提取。用欧几里得距离来衡量两张掌纹图像之间的差异。实验表明，使用QPCA可以获得更高的识别率。给定500棵棕榈树的3000个测试样本，最佳GAR为98.13%。

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Multispectral Palmprint Recognition Using Quaternion Principal Component Analysis

Abstract-Palmprint has been widely used in personal recognition. To improve the performance of the existing palmprint recognition system, multispectral palmprint recognition system has been proposed and designed. This paper presents a method of representing the multispectral palmprint images by quaternion and extracting features using the quaternion principal components analysis (QPCA) to achieve better performance in recognition. A data acquisition device is employed to capture the palmprint images under Red, Green, Blue and near-infrared (NIR) illuminations in less than 1s. QPCA is used to extract features of multispectral palmprint images. The dissimilarity between two palmprint images is measured by the Euclidean distance. The experiment shows that a higher recognition rate can be achieved when we use QPCA. Given 3000 testing samples from 500 palms, the best GAR is 98.13%.

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来源期刊

2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics

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