Flexible image segmentation and quality assessment for real-time iris recognition

The human iris has proved to be one of the most reliable biometric features for the identification of individuals. Real-time iris recognition requires high quality images that provide enough details about the iris texture and algorithms to analyze and process the images at the highest possible speed...

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Detalles Bibliográficos
Autores principales: Mottalli, M., Mejail, M., Jacobo-Berlles, J.
Formato: CONF
Materias:
Image edge analysis
Image segmentation
Real time systems
Biometrics
Digital image storage
Edge detection
Imaging systems
Interactive computer systems
Biometric features
Camera systems
High quality images
Human Iris
Identification of individuals
Iris images
Iris recognition
Iris texture
Quality assessment
Recognition rates
Segmentation results
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_15224880_v_n_p1941_Mottalli
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_15224880_v_n_p1941_Mottalli
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spelling todo:paper_15224880_v_n_p1941_Mottalli2023-10-03T16:20:45Z Flexible image segmentation and quality assessment for real-time iris recognition Mottalli, M. Mejail, M. Jacobo-Berlles, J. Image edge analysis Image segmentation Real time systems Biometrics Digital image storage Edge detection Imaging systems Interactive computer systems Real time systems Biometric features Camera systems High quality images Human Iris Identification of individuals Image edge analysis Iris images Iris recognition Iris texture Quality assessment Recognition rates Segmentation results Image segmentation The human iris has proved to be one of the most reliable biometric features for the identification of individuals. Real-time iris recognition requires high quality images that provide enough details about the iris texture and algorithms to analyze and process the images at the highest possible speed. In this work, an extension to the classical circular model for the pupil and iris using flexible contours is provided. Then, a method for assessing the quality of the iris images in real-time based on the segmentation results is introduced. Experimental results are presented, and we conclude that the new methods improve the recognition rate, achieving a 100% correct recognition rate on the CASIA iris database, while being suitable for a real-time iris recognition camera system. ©2009 IEEE. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15224880_v_n_p1941_Mottalli
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Image edge analysis
Image segmentation
Real time systems
Biometrics
Digital image storage
Edge detection
Imaging systems
Interactive computer systems
Real time systems
Biometric features
Camera systems
High quality images
Human Iris
Identification of individuals
Image edge analysis
Iris images
Iris recognition
Iris texture
Quality assessment
Recognition rates
Segmentation results
Image segmentation
spellingShingle Image edge analysis
Image segmentation
Real time systems
Biometrics
Digital image storage
Edge detection
Imaging systems
Interactive computer systems
Real time systems
Biometric features
Camera systems
High quality images
Human Iris
Identification of individuals
Image edge analysis
Iris images
Iris recognition
Iris texture
Quality assessment
Recognition rates
Segmentation results
Image segmentation
Mottalli, M.
Mejail, M.
Jacobo-Berlles, J.
Flexible image segmentation and quality assessment for real-time iris recognition
topic_facet Image edge analysis
Image segmentation
Real time systems
Biometrics
Digital image storage
Edge detection
Imaging systems
Interactive computer systems
Real time systems
Biometric features
Camera systems
High quality images
Human Iris
Identification of individuals
Image edge analysis
Iris images
Iris recognition
Iris texture
Quality assessment
Recognition rates
Segmentation results
Image segmentation
description The human iris has proved to be one of the most reliable biometric features for the identification of individuals. Real-time iris recognition requires high quality images that provide enough details about the iris texture and algorithms to analyze and process the images at the highest possible speed. In this work, an extension to the classical circular model for the pupil and iris using flexible contours is provided. Then, a method for assessing the quality of the iris images in real-time based on the segmentation results is introduced. Experimental results are presented, and we conclude that the new methods improve the recognition rate, achieving a 100% correct recognition rate on the CASIA iris database, while being suitable for a real-time iris recognition camera system. ©2009 IEEE.
format CONF
author Mottalli, M.
Mejail, M.
Jacobo-Berlles, J.
author_facet Mottalli, M.
Mejail, M.
Jacobo-Berlles, J.
author_sort Mottalli, M.
title Flexible image segmentation and quality assessment for real-time iris recognition
title_short Flexible image segmentation and quality assessment for real-time iris recognition
title_full Flexible image segmentation and quality assessment for real-time iris recognition
title_fullStr Flexible image segmentation and quality assessment for real-time iris recognition
title_full_unstemmed Flexible image segmentation and quality assessment for real-time iris recognition
title_sort flexible image segmentation and quality assessment for real-time iris recognition
url http://hdl.handle.net/20.500.12110/paper_15224880_v_n_p1941_Mottalli
work_keys_str_mv AT mottallim flexibleimagesegmentationandqualityassessmentforrealtimeirisrecognition
AT mejailm flexibleimagesegmentationandqualityassessmentforrealtimeirisrecognition
AT jacoboberllesj flexibleimagesegmentationandqualityassessmentforrealtimeirisrecognition
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