Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation

Detalhes bibliográficos
Autor(a) principal: De Marsico, Maria
Data de Publicação: 2017
Outros Autores: Nappi, Michele, Narduci, Fabio, Proença, H.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.6/9172
Resumo: Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural can- didate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular at- tention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies.
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spelling Insights into the results of MICHE I - Mobile Iris CHallenge EvaluationMobile Iris RecognitionEvaluationBiometric algorithm fusionMobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural can- didate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular at- tention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies.uBibliorumDe Marsico, MariaNappi, MicheleNarduci, FabioProença, H.2020-02-10T14:18:01Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9172eng10.1016/j.patcog.2017.08.028info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:49:49Zoai:ubibliorum.ubi.pt:10400.6/9172Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:20.518411Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
title Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
spellingShingle Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
De Marsico, Maria
Mobile Iris Recognition
Evaluation
Biometric algorithm fusion
title_short Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
title_full Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
title_fullStr Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
title_full_unstemmed Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
title_sort Insights into the results of MICHE I - Mobile Iris CHallenge Evaluation
author De Marsico, Maria
author_facet De Marsico, Maria
Nappi, Michele
Narduci, Fabio
Proença, H.
author_role author
author2 Nappi, Michele
Narduci, Fabio
Proença, H.
author2_role author
author
author
dc.contributor.none.fl_str_mv uBibliorum
dc.contributor.author.fl_str_mv De Marsico, Maria
Nappi, Michele
Narduci, Fabio
Proença, H.
dc.subject.por.fl_str_mv Mobile Iris Recognition
Evaluation
Biometric algorithm fusion
topic Mobile Iris Recognition
Evaluation
Biometric algorithm fusion
description Mobile biometrics technologies are nowadays the new frontier for secure use of data and services, and are considered particularly important due to the massive use of handheld devices in the entire world. Among the biometric traits with potential to be used in mobile settings, the iris/ocular region is a natural can- didate, even considering that further advances in the technology are required to meet the operational requirements of such ambitious environments. Aiming at promoting these advances, we organized the Mobile Iris Challenge Evaluation (MICHE)-I contest. This paper presents a comparison of the performance of the participant methods by various Figures of Merit (FoMs). A particular at- tention is devoted to the identification of the image covariates that are likely to cause a decrease in the performance levels of the compared algorithms. Among these factors, interoperability among different devices plays an important role. The methods (or parts of them) implemented by the analyzed approaches are classified into segmentation (S), which was the main target of MICHE-I, and recognition (R). The paper reports both the results observed for either S or R, and also for different recombinations (S+R) of such methods. Last but not least, we also present the results obtained by multi-classifier strategies.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01T00:00:00Z
2020-02-10T14:18:01Z
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dc.relation.none.fl_str_mv 10.1016/j.patcog.2017.08.028
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