Efficient iris and eyelids detection from facial sketch images
In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation proc...
Published in: | Electronic Letters on Computer Vision and Image Analysis |
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Universitat Autonoma de Barcelona
2018
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2-s2.0-85068485808 Boonchuan T.; Setumin S.; Radman A.; Suandi S.A. Efficient iris and eyelids detection from facial sketch images 2018 Electronic Letters on Computer Vision and Image Analysis 17 1 10.5565/rev/elcvia.1044 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068485808&doi=10.5565%2frev%2felcvia.1044&partnerID=40&md5=08fd628f5751437852e0f50d03ab0f6e In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation procedure. We limit the input face for the system to facial sketch photos with frontal pose, illumination invariant, neutral expression and without occlusions. CUHK and IIIT-D sketch databases are used to acquire the experimental results. As to validate the proposed algorithm, experiments on ground truth for iris and eyelids segmentation, which are prepared at our lab, is conducted. The iris segmentation from the proposed method gives the best accuracy of 92.93 and 86.71 based on F-measure evaluation for IIIT-D and CUHK, respectively. For eyelids segmentation, on the other hand, the proposed algorithm achieves an average of 4 standard deviation which indicates the closeness of proposed method to ground truth. © 2018 Computer Vision Center / Universitat Autonoma de Barcelona, Barcelona, Spain. Universitat Autonoma de Barcelona 15775097 English Article All Open Access; Green Open Access |
author |
Boonchuan T.; Setumin S.; Radman A.; Suandi S.A. |
spellingShingle |
Boonchuan T.; Setumin S.; Radman A.; Suandi S.A. Efficient iris and eyelids detection from facial sketch images |
author_facet |
Boonchuan T.; Setumin S.; Radman A.; Suandi S.A. |
author_sort |
Boonchuan T.; Setumin S.; Radman A.; Suandi S.A. |
title |
Efficient iris and eyelids detection from facial sketch images |
title_short |
Efficient iris and eyelids detection from facial sketch images |
title_full |
Efficient iris and eyelids detection from facial sketch images |
title_fullStr |
Efficient iris and eyelids detection from facial sketch images |
title_full_unstemmed |
Efficient iris and eyelids detection from facial sketch images |
title_sort |
Efficient iris and eyelids detection from facial sketch images |
publishDate |
2018 |
container_title |
Electronic Letters on Computer Vision and Image Analysis |
container_volume |
17 |
container_issue |
1 |
doi_str_mv |
10.5565/rev/elcvia.1044 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068485808&doi=10.5565%2frev%2felcvia.1044&partnerID=40&md5=08fd628f5751437852e0f50d03ab0f6e |
description |
In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation procedure. We limit the input face for the system to facial sketch photos with frontal pose, illumination invariant, neutral expression and without occlusions. CUHK and IIIT-D sketch databases are used to acquire the experimental results. As to validate the proposed algorithm, experiments on ground truth for iris and eyelids segmentation, which are prepared at our lab, is conducted. The iris segmentation from the proposed method gives the best accuracy of 92.93 and 86.71 based on F-measure evaluation for IIIT-D and CUHK, respectively. For eyelids segmentation, on the other hand, the proposed algorithm achieves an average of 4 standard deviation which indicates the closeness of proposed method to ground truth. © 2018 Computer Vision Center / Universitat Autonoma de Barcelona, Barcelona, Spain. |
publisher |
Universitat Autonoma de Barcelona |
issn |
15775097 |
language |
English |
format |
Article |
accesstype |
All Open Access; Green Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809677907141853184 |