应用一种多核稀疏表示模型实现掌纹分类

doi:10.3969/j.issn.1000-1158.2021.11.04

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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.

Key words： metrology palmprint images sparse representation kernel function based sparse representation multiple kernel functions feature classification

Received: 28 April 2020 Published: 01 December 2021

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TB96

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	Articles by authors
	SHANG Li
	ZHOU Yan
	SUN Zhan-li

Cite this article:

SHANG Li,ZHOU Yan,SUN Zhan-li. Palmprint Classification Using a Multi KernelSparse Representation Model[J]. Acta Metrologica Sinica, 2021, 42(11): 1430-1435.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.11.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I11/1430

［1］Shrivastava A, Patel V, Chellappa M. Multiple kernel learning for sparse representation-based classification［J］. IEEE Transactions on Image Processing, 2014, 23(7): 3013-3024.
［2］朱杰, 杨万扣, 唐振民, 等. 基于字典学习的核稀疏表示人脸识别方法［J］. 模式识别与人工智能, 2012, 25(5): 859-864.
Zhu J, Yang W K, Tang Z M, et al. A dictionary learning based kernel sparse representation method for face recognition［J］. Pattern Recognition and Artificial Intelligence, 2012, 25(5): 859-864.
［3］陈思宝, 许立仙, 罗斌, 等. 基于多重核的稀疏表示分类［J］. 电子学报, 2014, 42(9): 1807-1811.
Chen S B, Xu L X, Luo B, et al. Multiple kernel sparse representation based classification［J］. Acta Electronica Sinica, 2014, 42(9): 1807-1811.
［4］王行普, 俞璐. 混合核函数中权重求解方法［J］. 计算机系统应用, 2015, 24(4): 131-133.
Wang X P, Yu L. Weight solving method in mixed kernel function［J］. Computer System Application, 2015, 24(4): 131-133.
［5］陈杰, 尚丽. 基于核竞争学习算法的图像特征提取方法［J］. 计量学报, 2017, 38(5): 576-579.
Chen J, Shang L. Image Feature extraction using kernel winner-take-all based on independent component analysis algorithm［J］. Acta Metrologica Sinica, 2017, 38(5): 576-579.
［6］Zhang L, Zhou W D, Chang P C, et al. Kernel sparse representation based classifier［J］. IEEE Transactions on Signal Processing, 2012, 60(4): 1684-1695.
［7］Yin J, Liu Z H, Jin Z, et al. Kernel sparse representation based classification［J］. Neurocomputing, 2012, 77(1): 120-128.
［8］Huang H C, Chuang Y Y, Chen C S, et al. Multiple kernel fuzzy clustering［J］. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 120-134.
［9］陈永良, 李学斌. 基于核函数理论的系统聚类分析［J］. 吉林大学学报(地球科学版), 2010, 40(5): 1211-1216.
Chen Y L, Li X B. Kernel-Based Hierarchical Cluster Analysis［J］. Journal of Jilin University(Earth Science Edition), 2010, 40(5): 1211-1216.
［10］田源, 王洪涛. 基于量子核聚类算法的图像边缘特征提取研究［J］. 计量学报, 2016, 37(6): 582-586.
Tian Y, Wang H T. Image edge feature extraction research based on quantum kernel clustering algorithm［J］. Acta Metrologica Sinica, 2016, 37(6): 582-586.
［11］戴思薇, 吴小俊, 高翠芳, 等. 多核模糊聚类［J］. 计算机工程与应用, 2016, 52(2): 65-69.
Dai S W, Wu X J, Gao C F, et al. Multiple kernel fuzzy clustering［J］. Computer Engineerning and Applications, 2016, 52(2): 65-69.
［12］李海波, 曹云峰, 丁萌, 等. 基于三维点云聚类的坡度估计方法［J］. 计量学报, 2018, 39(3): 304-309.
Li H B, Cao Y F, Ding M, et al. A method of slope estimation based on clustering of three-dimensional point cloud［J］. Acta Metrologica Sinica, 2018, 39(3): 304-309.
［13］陈思宝, 赵令, 罗斌, 等. 基于局部保持的核稀疏表示字典学习［J］. 自动化学报, 2014, 40(10): 2295-2305.
Chen S B, Zhao L, Luo B, et al. Locality preserving bsed kernel dictionary learning for sparse representation ［J］. Acta Automatica Sinca, 2014, 40(10): 2295-2305.
［14］廖瑞华, 李勇帆, 刘宏, 等. 基于稳健主成分分析与核稀疏表示的人脸识别［J］. 计算机工程, 2016, 42(2): 200-205.
Liao D H, Li Y F, Liu H, et al. Face recognition based on robust principal component analysis and kernel sparse representation［J］. Computing Engineering, 2016, 42(2): 200-205.
［15］Scolkopf B, Smola A J, Müller K R, et al. Nonlinear component analysis as a kernel eigenvalue problem［J］. Neural Computation, 1998, 10(5): 1299-1319.
［16］Chen Z H, Zuo W M, Hu Q H, et al. Kernel sparse representation for time series classification［J］. Information Sciences, 2015, 292(20): 15-26.
［17］孟宗, 殷娜, 李晶, 等. 基于信号稀疏表示和瞬态冲激信号多特征提取的滚动轴承故障诊断［J］. 计量学报,2019, 40(5): 855-860.
Meng Z, Yin N, Li J, et al. Fault diagnosis of rolling bearing based on sparse representation of signals and transient impulse signal multifeature extraction［J］. Acta Metrologica Sinica, 2019, 40(5): 855-860.
［18］Connie T, Teoh A, Goh M, et al. Palmprint recognition with PCA and ICA［C］// Image and Vision Computing. Palmerston North, New Zealand, 2003: 227-232.