Image factorization and feature fusion for enhancing robot vision in human face recognition

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Abstract

Illumination variation has been a challenging problem for face recognition in robot vision. To reduce the effect caused by illumination variation, a lot of studies have been explored. The Total Variation (TV) method is particular used to
factorize images into a low frequency component and a high frequency one. However, the low frequency component still contains significant intrinsic features resulting in failure in face recognition in some cases. In this paper, we propose to further extract illumination invariant features from face images under
uncontrolled varying lighting conditions. The Nonsampled Contourlet Transform (NSCT) method is employed to enhance the extraction of intrinsic feature. The combined factorization model is very effective in the experiment on the Yale database.
Original languageEnglish
Title of host publicationProceedings of the 2014 international joint conference on neural networks (IJCNN 2014)
PublisherIEEE
Pages981-986
ISBN (Electronic)2161-4393 (ISSN)
ISBN (Print)978-1-4799-1482-1
DOIs
Publication statusPublished - Nov 2014
EventIEEE 2014 International Joint Conference on Neural Networks - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Conference

ConferenceIEEE 2014 International Joint Conference on Neural Networks
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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