Droopy Mouth Detection Model in stroke warning

This paper presents a Droopy Mouth Detection Model in stroke warning. The objective of this paper is to take up the challenge to provide early detection of stroke through mouth drooping detection in mobile Android platform. To achieve that, a specialized library, Google Mobile Vision is utilized to...

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Main Authors: Foong, O.-M., Hong, K.-W., Yong, S.-P.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010433076&doi=10.1109%2fICCOINS.2016.7783286&partnerID=40&md5=49f25b88dbefab6ac0cccdc776dffc47
http://eprints.utp.edu.my/30481/
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spelling my.utp.eprints.304812022-03-25T06:55:34Z Droopy Mouth Detection Model in stroke warning Foong, O.-M. Hong, K.-W. Yong, S.-P. This paper presents a Droopy Mouth Detection Model in stroke warning. The objective of this paper is to take up the challenge to provide early detection of stroke through mouth drooping detection in mobile Android platform. To achieve that, a specialized library, Google Mobile Vision is utilized to detect facial landmark such as mouth corners and obtain the coordinates of the landmarks or key points for further processing. The inputs for the proposed droopy mouth detection model were taken from the Google Web Images and National Cheng Kung University (NCKU) Robotics Face datasets. The system prototype was evaluated using metrics such as Precision, Recall and F-Score to determine its recognition rate. Experimental results show that the proposed droopy mouth detection model has achieved satisfactory recognition rate. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010433076&doi=10.1109%2fICCOINS.2016.7783286&partnerID=40&md5=49f25b88dbefab6ac0cccdc776dffc47 Foong, O.-M. and Hong, K.-W. and Yong, S.-P. (2016) Droopy Mouth Detection Model in stroke warning. In: UNSPECIFIED. http://eprints.utp.edu.my/30481/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper presents a Droopy Mouth Detection Model in stroke warning. The objective of this paper is to take up the challenge to provide early detection of stroke through mouth drooping detection in mobile Android platform. To achieve that, a specialized library, Google Mobile Vision is utilized to detect facial landmark such as mouth corners and obtain the coordinates of the landmarks or key points for further processing. The inputs for the proposed droopy mouth detection model were taken from the Google Web Images and National Cheng Kung University (NCKU) Robotics Face datasets. The system prototype was evaluated using metrics such as Precision, Recall and F-Score to determine its recognition rate. Experimental results show that the proposed droopy mouth detection model has achieved satisfactory recognition rate. © 2016 IEEE.
format Conference or Workshop Item
author Foong, O.-M.
Hong, K.-W.
Yong, S.-P.
spellingShingle Foong, O.-M.
Hong, K.-W.
Yong, S.-P.
Droopy Mouth Detection Model in stroke warning
author_facet Foong, O.-M.
Hong, K.-W.
Yong, S.-P.
author_sort Foong, O.-M.
title Droopy Mouth Detection Model in stroke warning
title_short Droopy Mouth Detection Model in stroke warning
title_full Droopy Mouth Detection Model in stroke warning
title_fullStr Droopy Mouth Detection Model in stroke warning
title_full_unstemmed Droopy Mouth Detection Model in stroke warning
title_sort droopy mouth detection model in stroke warning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010433076&doi=10.1109%2fICCOINS.2016.7783286&partnerID=40&md5=49f25b88dbefab6ac0cccdc776dffc47
http://eprints.utp.edu.my/30481/
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score 13.159267