Localized multifeature metric learning for image-set-based face recognition

Jiwen Lu, Gang Wang, Pierre Moulin

Research output: Contribution to journalArticle


This paper presents a new approach to image-set-based face recognition, where each training and testing example is a set of face images captured from varying poses, illuminations, expressions, and resolutions. While a number of image set based face recognition methods have been proposed in recent years, most of them model each face image set as a single linear subspace or as the union of linear subspaces, which may lose some discriminative information for face image set representation. To address this shortcoming, we propose exploiting statistics information as feature representations for face image sets and develop a localized multikernel metric learning algorithm to effectively combine different statistics for recognition. Moreover, we propose a localized multikernel multimetric learning method to jointly learn multiple feature-specific distance metrics in the kernel spaces, one for each statistic feature, to better exploit complementary information for recognition. Our methods achieve state-of-the-art performance on four widely used video face datasets including the Honda, MoBo, YouTube Celebrities, and YouTube Face datasets.

Original languageEnglish (US)
Article number7060677
Pages (from-to)529-540
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number3
StatePublished - Mar 2016


  • Face recognition
  • image set classification
  • metric learning
  • multi-kernel learning
  • multi-metric learning

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Localized multifeature metric learning for image-set-based face recognition'. Together they form a unique fingerprint.

  • Cite this