A discriminative metric learning algorithm for face recognition

Tsuyoshi Kato, Wataru Takei, Shinichiro Omachi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Face recognition is a multi-class classification problem that has long attracted many researchers in the community of image analysis. We consider using the Mahalanobis distance for the task. Classically, the inverse of a covariance matrix has been chosen as the Mahalanobis matrix, a parameter of the Mahalanobis distance. Modern studies often employ machine learning algorithms called metric learning to determine the Mahalanobis matrix so that the distance is more discriminative, although they resort to eigen-decomposition requiring heavy computation. This paper presents a new metric learning algorithm that finds discriminative Mahalanobis matrices efficiently without eigen-decomposition, and shows promising experimental results on real-world face-image datasets.

Original languageEnglish
Pages (from-to)85-89
Number of pages5
JournalIPSJ Transactions on Computer Vision and Applications
Volume5
DOIs
Publication statusPublished - 2013 Jul 1

Keywords

  • Face recognition
  • Mahalanobis distance
  • Metric learning
  • Nearest neighbor classifier
  • Optimization

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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