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Occluded Face Recognition Using Correntropy-based Nonnegative Matrix Factorization

Author
Zurada, Jacek M.
Ensari, Tolga
Chorowski, Jan
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Abstract
Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function. In this paper we propose to maximize the correntropy similarity measure as an objective function for NMF. Correntropy has been recently defined as a nonlinear similarity measure using an entropy-based criterion. After the minimization process of the correntropy function, we use it to recognize occluded face data set and compare its recognition performance with the standard NMF and Principal Component Analysis (PCA). The experimental results are illustrated with ORL face data set. The results show that our correntropy-based NMF (NMF-Corr) has better recognition rate compared with PCA and NMF.
URI
http://hdl.handle.net/20.500.12627/109544
https://doi.org/10.1109/icmla.2012.112
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İstanbul Üniversitesi Akademik Arşiv Sistemi (ilgili içerikte aksi belirtilmediği sürece) Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

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Creative Commons Lisansı

İstanbul Üniversitesi Akademik Arşiv Sistemi (ilgili içerikte aksi belirtilmediği sürece) Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

DSpace software copyright © 2002-2016  DuraSpace
Contact Us | Send Feedback
Theme by 
Atmire NV