DocumentCode :
2859554
Title :
Repeated Measures GLMM Estimation of Subject-Related and False Positive Threshold Effects on Human Face Verification Performance
Author :
Givens, Geof H. ; Beveridge, J. Ross ; Draper, Bruce A. ; Phillips, P. Jonathon
Author_Institution :
Statistics Department, Colorado State University
fYear :
2005
fDate :
25-25 June 2005
Firstpage :
40
Lastpage :
40
Abstract :
Subject covariate data were collected on 1, 072 pairs of FERET images for analysis in a human face verification experiment. The subject data included information about facial hair, bangs, eyes, gender, and age. The verification experiment was replicated at seven different false alarm rates ranging from 1/10, 000 to 1/100. A generalized linear mixed model (GLMM) was fit to the binary outcomes indicating correct verification. Statistically significant main effects for bangs, eyes, gender, and age were found. The effect of the log false positive rate on verification success was found to interact significantly with bangs, gender, and age. These results have important implications for future evaluation of biometrics, and the GLMM methodology used here is shown to be effective and informative for this sort of data.
Keywords :
Computer science; Eyes; Face recognition; Humans; NIST; Pattern recognition; Performance analysis; Statistical analysis; Statistics; Virtual reality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on
Conference_Location :
San Diego, CA, USA
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.520
Filename :
1565338
Link To Document :
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