Face representation using independent component analysis

Pong C. Yuen*, J. H. Lai

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

168 Citations (Scopus)
4 Downloads (Pure)


This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT Al Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.

Original languageEnglish
Pages (from-to)1247-1257
Number of pages11
JournalPattern Recognition
Issue number6
Publication statusPublished - Jun 2002

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Independent component analysis
  • Principal component analysis
  • Face recognition


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