Bayesian face recognition
Pattern RecognitionPublished 1 November 2000
Baback Moghaddam, Tony Jebara, Alex Pentland
Citations617
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
A simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear subspace projections and simple Euclidean norms is derived, thus resulting in a significant computational speed-up for implementation with very large databases.
Abstract
We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA's 1996 "FERET" face recognition competition, in which this Bayesian matching alogrithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases.
Keywords
Computer Science
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