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...

Full description

Bibliographic Details
Published in:Electronic Letters on Computer Vision and Image Analysis
Main Author: Boonchuan T.; Setumin S.; Radman A.; Suandi S.A.
Format: Article
Language:English
Published: Universitat Autonoma de Barcelona 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068485808&doi=10.5565%2frev%2felcvia.1044&partnerID=40&md5=08fd628f5751437852e0f50d03ab0f6e
id 2-s2.0-85068485808
spelling 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