Rotation Invariant Iris Recognition Method Adaptive to Ambient Lighting Variation

Hironobu TAKANO; Hiroki KOBAYASHI; Kiyomi NAKAMURA

doi:10.1093/ietisy/e90-d.6.955

Rotation Invariant Iris Recognition Method Adaptive to Ambient Lighting Variation

Hironobu TAKANO, Hiroki KOBAYASHI, Kiyomi NAKAMURA

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We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.

Publication: IEICE TRANSACTIONS on Information Vol.E90-D No.6 pp.955-962

Publication Date: 2007/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e90-d.6.955

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

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Hironobu TAKANO, Hiroki KOBAYASHI, Kiyomi NAKAMURA, "Rotation Invariant Iris Recognition Method Adaptive to Ambient Lighting Variation" in IEICE TRANSACTIONS on Information, vol. E90-D, no. 6, pp. 955-962, June 2007, doi: 10.1093/ietisy/e90-d.6.955.
Abstract: We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.
URL: https://globals.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.6.955/_p

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@ARTICLE{e90-d_6_955,
author={Hironobu TAKANO, Hiroki KOBAYASHI, Kiyomi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Rotation Invariant Iris Recognition Method Adaptive to Ambient Lighting Variation},
year={2007},
volume={E90-D},
number={6},
pages={955-962},
abstract={We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.},
keywords={},
doi={10.1093/ietisy/e90-d.6.955},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Rotation Invariant Iris Recognition Method Adaptive to Ambient Lighting Variation
T2 - IEICE TRANSACTIONS on Information
SP - 955
EP - 962
AU - Hironobu TAKANO
AU - Hiroki KOBAYASHI
AU - Kiyomi NAKAMURA
PY - 2007
DO - 10.1093/ietisy/e90-d.6.955
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2007
AB - We previously proposed a rotation-spreading neural network (R-SAN net). This neural net can recognize the orientation of an object irrespective of its shape, and its shape irrespective of its orientation. The R-SAN net is suitable for orientation recognition of a concentric circular pattern such as an iris image. Previously, variations of ambient lighting conditions affected iris detection. In this study, we introduce brightness normalization for accuracy improvement of iris detection in various lighting conditions. Brightness normalization provides high accuracy iris extraction in severe lighting conditions. A recognition experiment investigated the characteristics of rotation and shape recognition for both learned and un-learned iris images in various plane rotations. The R-SAN net recognized the rotation angle for the learned iris images in arbitrary orientation, but not for un-learned iris images. Thus, the variation of the rotation angle was corrected only for learned irises, but not un-learned irises. Although the R-SAN net rightly recognized the learned irises, it could not completely reject the un-learned irises as unregistered irises. Using the specific orientation recognition characteristics of the R-SAN net, a minimum distance was introduced as a new shape recognition criterion for the R-SAN net. In consequence, the R-SAN net combined with the minimum distance rightly recognized both learned (registered) and un-learned irises; the unregistered irises were correctly rejected.
ER -