Face recognition by concept formation neural structure

Yosuke Koyanaka, Noriyasu Homma, Masao Sakai, Kenichi Abe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we develop a new neural model that deals with continuation value of inputs for some practical applications of pattern recognition task. An essential core of the model is use of a novel vector representation of a target concept in a multi-level informational hierarchy that makes the model possess category formation ability from incomplete observation of the target. Simulation results demonstrate the usefulness of the model for a facial image recognition task, even if it is carried out under an incremental and unsupervised learning environment.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages1483-1487
Number of pages5
Publication statusPublished - 2004
EventSICE Annual Conference 2004 - Sapporo, Japan
Duration: 2004 Aug 42004 Aug 6

Other

OtherSICE Annual Conference 2004
CountryJapan
CitySapporo
Period04/8/404/8/6

Keywords

  • Concept formation
  • Hebbian rule
  • Incremental learning
  • Neural networks
  • Pattern recognition
  • Self-organization

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Face recognition by concept formation neural structure'. Together they form a unique fingerprint.

Cite this