Recognizing faces with PCA and ICA

  • Authors:

  • Bruce A. Draper;Kyungim Baek;Marian Stewart Bartlett;J. Ross Beveridge

  • Affiliations:

  • Department of Computer Science, Colorado State University, Ft. Collins, CO;Department of Biomedical Engineering, Columbia University, New York, NY;Institute for Neural Computation, University of California San Diego, La Jolla, CA;Department of Computer Science, Colorado State University, Ft. Collins, CO

  • Venue:
  • Computer Vision and Image Understanding - Special issue on Face recognition
  • Year:
  • 2003

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Abstract

This paper compares principal component analysis (PCA) and independent component analysis (ICA) in the context of a baseline face recognition system, a comparison motivated by contradictory claims in the literature. This paper shows how the relative performance of PCA and ICA depends on the task statement, the ICA architecture, the ICA algorithm, and (for PCA) the subspace distance metric. It then explores the space of PCA/ICA comparisons by systematically testing two ICA algorithms and two ICA architectures against PCA with four different distance measures on two tasks (facial identity and facial expression). In the process, this paper verifies the results of many of the previous comparisons in the literature, and relates them to each other and to this work. We are able to show that the FastICA algorithm configured according to ICA architecture II yields the highest performance for identifying faces, while the InfoMax algorithm configured according to ICA architecture II is better for recognizing facial actions. In both cases, PCA performs well but not as well as ICA.