Query-driven locally adaptive fisher faces and expert-model for face recognition

Yun Fu, Junsong Yuan, Zhu Li, Thomas S. Huang, Ying Wu

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

Abstract

We present a novel expert-model of Query-Driven Locally Adaptive (QDLA) Fisher faces for robust face recognition. For each query face, the proposed method first fits local Fisher models with different appearances. A hybrid expert model then integrates these local models and combines the classification results based on the estimated error rate for each local model. This approach addresses the large size recognition problem, where many local variations can not be adequately handled by a single global model in a single appearance space. To speed up the query process, Locality Sensitive Hash(LSH) is applied for fast nearest neighbor search. Experiments demonstrate the approach to be effective, robust, and fast for large size, multi-class, and multi-variance data sets.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PagesI141-I144
DOIs
StatePublished - Dec 1 2007
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume1
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
Country/TerritoryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • Expert model
  • Face recognition
  • Fisher face
  • Locality sensitive hash
  • Nearest neighbor
  • Query

ASJC Scopus subject areas

  • Engineering(all)

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