Face recognition using a new distance metric

Author

Partridge, Matthew ; Jabri, Marwan

Author_Institution

Sch. of Electr. & Inf. Eng., Sydney Univ., NSW, Australia

Volume

2

fYear

2000

fDate

2000

Firstpage

584

Abstract

Many classification techniques use a distance metric as a measure of the similarity between patterns, and their generalisation performance is often strongly related to the effectiveness of the measure. This paper introduces a distance metric based on the Mahalanobis distance function, which is statistically more reliable than some metrics but does not discard discriminating information, often regarded as “noise”. In addition, it may be computed quickly. This paper develops this metric and experimentally shows that it may be used in a classifier to give the lowest error rate (2.63%) as well as the best training and classification times for a face recognition task

Keywords

face recognition; generalisation (artificial intelligence); image classification; learning (artificial intelligence); statistics; Mahalanobis distance function; classification technique; computation speed; discriminating information; distance metric; error rate; face recognition; generalisation performance; noise; pattern similarity measure; statistical reliability; training; Bayesian methods; Classification tree analysis; Decision trees; Electronic mail; Error analysis; Face recognition; Frequency estimation; Gaussian distribution; Humans; Pattern classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop

Conference_Location

Sydney, NSW

ISSN

1089-3555

Print_ISBN

0-7803-6278-0

Type

conf

DOI

10.1109/NNSP.2000.890137

Filename

890137