Face recognition using wavelet transform and non-negative matrix factorization

Authors:
Neo Han Foon;Andrew Teoh Beng Jin;David Ngo Chek Ling
Affiliations:
Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia;Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia;Faculty of Information Science and Technology (FIST), Multimedia University, Melaka, Malaysia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

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).