|Table of Contents|

A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2016年04期
Page:
0-
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
A Kernel Sparse Representation Method Based onSamples Fusion for Face Recognition
Author(s):
Shen Xuehua12Zhan Yongzhao2Cheng Xianyi1Ding Weiping1
(1.School of Computer Science & Technology,Nantong University,Nantong 226019,China)(2.School of Computer Science & Communications Engineering,Jiangsu University,Zhenjiang 212013,China)
Keywords:
face recognitionsamples fusionkernel-inducedsparse
PACS:
TP391.4
DOI:
10.3969/j.issn.1001-4616.2016.04.007
Abstract:
To improve the recognition rate of face recognition method based on small sample set of face images and reduce the high computational complexity,a kernel sparse representation method based on samples fusion method(KSRMSF)is proposed. The proposed method first extends the training samples to form a new training set by adding some mirror virtual training samples and symmetrical ones,and uses a algorithm based on Gaussian Kernel Function to determine N nearest neighbors of the testing sample from the new training samples. Finally,the KSRMSF represents the testing sample as a linear combination of the determinated N nearest neighbors and performs the classification according to the L2 norm. Through the different values of N set,the classification is more accurate. Many experiments show that the KSRMSF can get a better classification result than the same type of algorithm.

References:

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Last Update: 2016-12-31