Discriminant waveletfaces and nearest feature classifiers for face recognition

Jen-Tzung Chien*, Chia Chen Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

395 Scopus citations

Abstract

Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents hybrid approaches to handle three issues together. For feature extraction, we apply the multiresolution wavelet transform to extract waveletlace. We also perform the linear discriminant analysis on waveletfaces to reinforce discriminant power. During classification, the nearest feature plane (NFP) and nearest feature space (NFS) classifiers are explored for robust decision in presence of wide facial variations. Their relationships to conventional nearest neighbor and nearest feature line classifiers are demonstrated. In the experiments, the discriminant waveletface incorporated with the NFS classifier achieves the best face recognition performance.

Original languageEnglish
Pages (from-to)1644-1649
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume24
Issue number12
DOIs
StatePublished - 1 Dec 2002

Keywords

  • Discriminant waveletface
  • Face recognition
  • Nearest feature classifier

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