Face recognition using wavelet transform and non-negative matrix factorization


Foon, , NH and Ling, , DNC and Jin, , ATB (2004) Face recognition using wavelet transform and non-negative matrix factorization. AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 3339 . pp. 192-202. ISSN 0302-9743

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This paper demonstrates a novel subspace projection technique via Non-Negative Matrix Factorization (NMF) to represent human facial image in low frequency subband, which is able to realize through the wavelet transform. Wavelet transform (WT), is used to reduce the noise and produce a representation in the low frequency domain, and hence making the facial images insensitive to facial expression and small occlusion. After wavelet decomposition, NMF is performed to produce region or part-based representations of the images. Non-negativity is a useful constraint to generate expressiveness in the reconstruction of faces. The simulation results on Essex and ORL database show that the hybrid of NMF and the best wavelet filter will yield better verification rate and shorter training time. The optimum results of 98.5% and 95.5% are obtained from Essex and ORL Database, respectively. These results are compared with our baseline method, Principal Component Analysis (PCA).

Item Type: Article
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 22 Aug 2011 06:49
Last Modified: 22 Aug 2011 06:49
URII: http://shdl.mmu.edu.my/id/eprint/2517


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