Palmprint Classification Using a Multi KernelSparse Representation Model
SHANG Li1,ZHOU Yan1,SUN Zhan-li2
1. School of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu 215104, China
2. School of Electronic and Automation, Anhui University, Hefei, Anhui 230039, China
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.
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2021, Vol. 42 
