Probabilistic reasoning models for face recognition

Chengjun Liu, Harry Wechsler

Research output: Contribution to journalConference articlepeer-review

37 Scopus citations

Abstract

We introduce in this paper two probabilistic reasoning models (PRM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition - the Eigenfaces and Fisherfaces.

Original languageEnglish (US)
Pages (from-to)827-832
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - Dec 1 1998
Externally publishedYes
EventProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
Duration: Jun 23 1998Jun 25 1998

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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