Face recognition using wavelet transform and non-negative matrix factorization

N. H. Foon, A. T.B. Jin, D. N.C. Ling

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)192-202
Number of pages11
JournalLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Publication statusPublished - 2004
Event17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia
Duration: 2004 Dec 42004 Dec 6

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Face recognition using wavelet transform and non-negative matrix factorization'. Together they form a unique fingerprint.

Cite this