NDLI: A novel constraint non-negative matrix factorization criterion based incremental learning in face recognition

Content Provider	IEEE Xplore Digital Library
Author	Wen-Sheng Chen Bin-Bin Pan Bin Fang Jin Zou
Copyright Year	2008
Description	Author affiliation: Dept. of Math., Leshan Teachers Coll., Leshan (Wen-Sheng Chen)
Abstract	This paper addresses incremental learning and time-consuming problems in non-negative matrix factorization (NMF) of face recognition. When the training samples or classes are incremental, almost all existing NMF based methods must implement repetitive learning. Also, they are usually very time-consuming. To overcome these limitations, we proposed a novel constraint block NMF (CBNMF) method, which is based on a new constraint NMF criterion and our previous block technique in NMF. CMU PIE face database is selected for evaluation. Comparing with Block NMF (BNMF), NMF and PCA methods, experimental results show that our proposed CBNMF approach gives the best performance.
Starting Page	292
Ending Page	297
File Size	589152
Page Count	6
File Format	PDF
ISBN	9781424422388
DOI	10.1109/ICWAPR.2008.4635792
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Databases Face recognition Incremental Learning Non-negative Matrix Factorization Matrix decomposition Face Face Recognition Principal component analysis Manganese
Content Type	Text
Resource Type	Article

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A novel constraint non-negative matrix factorization criterion based incremental learning in face recognition