Face recognition based on regularized nearest points between image sets

Meng Yang, Pengfei Zhu, Luc Van Gool, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

115 Citations (Scopus)

Abstract

In this paper, a novel regularized nearest points (RNP) method is proposed for image sets based face recognition. By modeling an image set as a regularized affine hull (RAH), two regularized nearest points (RNP), one on each image set's RAH, are automatically determined by an efficient iterative solver. The between-set distance of RNP is then defined by considering both the distance between the RNPs and the structure of image sets. Compared with the recently developed sparse approximated nearest points (SANP) method, RNP has a more concise formulation, less variables and lower time complexity. Extensive experiments on benchmark databases (e.g., Honda/UCSD, CMU Mobo and YouTube databases) clearly show that our proposed RNP consistently outperforms state-of-the-art methods in both accuracy and efficiency.
Original languageEnglish
Title of host publication2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
DOIs
Publication statusPublished - 20 Aug 2013
Event2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013 - Shanghai, China
Duration: 22 Apr 201326 Apr 2013

Conference

Conference2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
CountryChina
CityShanghai
Period22/04/1326/04/13

Keywords

  • face recognition
  • image set
  • regularized affine hull
  • regularized nearest points

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this