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Titolo:
Statistical richness of visual phase information: Update on recognizing persons by iris patterns
Autore:
Daugman, J;
Indirizzi:
Univ Cambridge, Comp Lab, Cambridge CB3 0DF, England Univ Cambridge Cambridge England CB3 0DF Lab, Cambridge CB3 0DF, England
Titolo Testata:
INTERNATIONAL JOURNAL OF COMPUTER VISION
fascicolo: 1, volume: 45, anno: 2001,
pagine: 25 - 38
SICI:
0920-5691(200110)45:1<25:SROVPI>2.0.ZU;2-Z
Fonte:
ISI
Lingua:
ENG
Soggetto:
FACE-RECOGNITION;
Keywords:
statistical independence; wavelets; texture; iris recognition; decision theory;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Daugman, J Univ Cambridge, Comp Lab, Cambridge CB3 0DF, England Univ Cambridge Cambridge England CB3 0DF dge CB3 0DF, England
Citazione:
J. Daugman, "Statistical richness of visual phase information: Update on recognizing persons by iris patterns", INT J COM V, 45(1), 2001, pp. 25-38

Abstract

Algorithms first described in 1993 for recognizing persons by their iris patterns have now been tested in several public field trials, producing no false matches in several million comparison tests. The underlying recognition principle is the failure of a test of statistical independence on texturephase structure as encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 244 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm(2) over the iris, enabling real-time decisions about personalidentity with extremely high confidence. This paper reviews the current algorithms and presents the results of 2.3 million comparisons among eye images acquired in trials in Britain, the USA, and Japan, and it discusses aspects of the process still in need of improvement.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 22/01/20 alle ore 06:54:49