|Table of Contents|

Occlusion face recognition based on robust principal component analysis and low rank(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年04期
Page:
460-
Research Field:
Publishing date:

Info

Title:
Occlusion face recognition based on robust principal component analysis and low rank
Author(s):
Tang XianHuang Junwei
Department of Cmputer Engineering,Shangqiu University,Shangqiu 476000,China
Keywords:
robust principal component analysis pattern recognition occlusion face low rank mapping error rate
PACS:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.010
Abstract:
In order to improve the recognition accuracy of occlusion face,a novel occlusion face recognition algorithm combining robust principal component analysis and low rank is proposed.Firstly,face images are collected and are correspondingly pretreated,and secondly the face samples are decomposed by using robust principal component analysis to obtain low rank data matrix and sparse error matrix,and face images of training samples and testing samples are established.At last face is weighted and recognized according to the error matrix,the classic face database is used to carried out simulation experiment.The results show that the proposed algorithm has improved the occluded face recognition accuracy significantly,effectively reduces the error rate of the occluded face,and has better robustness.

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Last Update: 2017-08-31