Preliminary studies on the Good, the Bad, and the Ugly face recognition challenge problem

Yui Man Lui, David Bolme, P. Jonathon Phillips, J. Ross Beveridge, Bruce A. Draper

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

Face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performance envelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challenge problem has been introduced to focus on hard aspects of face recognition from still frontal images. In this paper, we introduce the CohortLDA baseline algorithm, which is an Linear Discriminant Analysis (LDA) algorithm with color spaces and cohort normalization. CohortLDA greatly outperforms some well known face recognition algorithms on the GBU challenge problem. The GBU protocol includes rules for creating training sets. We investigate the effect on performance of violating the rules for creating training sets. This analysis shows that violating the GBU protocol can substantially over estimate performance on the GBU challenge problem.

Original languageEnglish
Title of host publication2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
Pages9-16
Number of pages8
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012 - Providence, RI, United States
Duration: Jun 16 2012Jun 21 2012

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
Country/TerritoryUnited States
CityProvidence, RI
Period06/16/1206/21/12

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