“Dynamically Visual Learning for Person Identification with Sparsely Distributed Multiple Cameras”

Hidenori Tanaka, Hideo Saito, Itaru Kitahara, Hiroshi Murase, Kiyoshi Kogure, Norihiro Hagita

Research output: Contribution to journalArticlepeer-review


This paper proposes a dynamical visual learning method, which aims to identify person using multiple surveillance cameras sparsely distributed in space. In the proposed method, densely distributed multiple images are captured by interpolating the object' s appearance in the sparsely distributed multiple images with a simple 3D face model, and generate two initial eigenspaces (an eigenspace for pose estimation and that for identification). In case another image is captured, the object's pose and name are estimated using the eigenspaces. The image is projected onto the 3D face model as texture information to improve the object's appearance, and the eigenspaces are regenerated. The discernment capability for person identification of the proposed method is shown by experimental results.

Original languageEnglish
Pages (from-to)623-633
Number of pages11
JournalJournal of the Institute of Image Electronics Engineers of Japan
Issue number5
Publication statusPublished - 2009 Jan


  • 3D face model
  • person identification
  • surveillance cameras
  • view interpolation
  • visual learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Electrical and Electronic Engineering


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