2018
DOI: 10.5815/ijigsp.2018.11.04
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Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Abstract: This research presents a framework to detect a questionable observer depending on a specific activity named "frequent iris movement". We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation … Show more

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Cited by 2 publications
(3 citation statements)
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“…The Fig.16 mark for the Public Transportation Field shows the precision of the identification work of Elhamod et al The BEHAVE CAVIAR and PETS 2006 data collection has achieved an accuracy of 66 percent [38]. The second last bar represents accuracy [20] with a data set of its own. They also reached an accuracy of 93 percent.…”
Section: Analysis and Discussionmentioning
confidence: 96%
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“…The Fig.16 mark for the Public Transportation Field shows the precision of the identification work of Elhamod et al The BEHAVE CAVIAR and PETS 2006 data collection has achieved an accuracy of 66 percent [38]. The second last bar represents accuracy [20] with a data set of its own. They also reached an accuracy of 93 percent.…”
Section: Analysis and Discussionmentioning
confidence: 96%
“…[15][16][17][18][19]); as far as we know, none of the behaviors listed above has been identified for dubious observer detection. Very little research has been conducted on irregular behavior identification [21] and dubious spectator detection [20,22,23] and the identification of disruptive crowd movements [24].…”
Section: Introductionmentioning
confidence: 99%
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