Linear projection methods in face recognition under unconstrained illuminations: a comparative study

  • Authors:

  • Qi Li;Jieping Ye;Chandra Kambhamettu

  • Affiliations:

  • Video, Image Modeling and Synthesis Lab, Computer Information & Science, University of Delaware, Newark, DE;Computer Science & Engineering, University of Minnesota, Minneapolis, MN;Video, Image Modeling and Synthesis Lab, Computer Information & Science, University of Delaware, Newark, DE

  • Venue:
  • CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2004

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Abstract

Face recognition under unconstrained illuminations (FR/I) received extensive study because of the existence of illumination subspace. [2] presented a study on the comparison between Principal component analysis (PCA) and subspace Linear Discriminant Analysis (LDA) for this problem. PCA and subspace LDA are two well-known linear projection methods that can be characterized as trace optimization on scatter matrices. Generally, a linear projection method can be derived by applying a specific matrix analysis technique on specific scatter matrices under some optimization criterion. Several novel linear projection methods were proposed recently using Generalized Singular Value Decomposition or QR Decomposition matrix analysis techniques [10, 17, 11]. In this paper, we present a comparative study on these linear projection methods in FR/I. We further involve multiresolution analysis in the study. Our comparative study is expected to give a relatively comprehensive view on the performance of linear projection methods in FR/I problems.