应用一种多核稀疏表示模型实现掌纹分类

尚丽; 周燕; 孙战里

doi:10.3969/j.issn.1000-1158.2021.11.04

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计量学报 ›› 2021, Vol. 42 ›› Issue (11) : 1430-1435. DOI: 10.3969/j.issn.1000-1158.2021.11.04

光学计量

应用一种多核稀疏表示模型实现掌纹分类

尚丽¹,周燕¹,孙战里²

作者信息 +

Palmprint Classification Using a Multi KernelSparse Representation Model

SHANG Li¹,ZHOU Yan¹,SUN Zhan-li²

Author information +

文章历史 +

摘要

与稀疏表示(SR)模型相比,基于单个核函数的SR(KSR)模型可以有效减少数据维数、降低学习模型的计算复杂度并提高特征分类精度;但这种模型对核函数及其参数的选择通常不能包含恰当的、完整的分类信息。为了满足更高的特征分类精度需求,提出了一种基于多个核函数的SR(M-KSR)模型及其快速稀疏优化方法,并将其应用于掌纹图像的分类。测试结果证明了基于M-KSR模型的掌纹分类方法的有效性和实用性。

Abstract

Compared with sparse representation (SR) model, signal kernel function based SR (KSR) model, can reduce efficiently the number of dimensions of data and computational complexity of this learning model, as also as improve the feature classification accuracy. However, the proposed model usually doesn’t contain proper and complete classification information for the selection of kernel functions and their corresponding parameters, therefore, in order to meet the needs of higher feature classification accuracy, a multiple kernel function based KSR (M-KSR) model and its fast sparse optimization method are proposed here, and the proposed model is used to classify palmprint images. Test results prove that the M-KSR model based palmprint classification method is very efficient and applicable in practice.

导出引用

尚丽,周燕,孙战里. 应用一种多核稀疏表示模型实现掌纹分类[J]. 计量学报. 2021, 42(11): 1430-1435 https://doi.org/10.3969/j.issn.1000-1158.2021.11.04

SHANG Li,ZHOU Yan,SUN Zhan-li. Palmprint Classification Using a Multi KernelSparse Representation Model[J]. Acta Metrologica Sinica. 2021, 42(11): 1430-1435 https://doi.org/10.3969/j.issn.1000-1158.2021.11.04

中图分类号： TB96

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