@inproceedings{scholars6477,
           pages = {616--621},
           title = {Droopy Mouth Detection Model in stroke warning},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings},
             doi = {10.1109/ICCOINS.2016.7783286},
            year = {2016},
            note = {cited By 10; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433},
          author = {Foong, O.-M. and Hong, K.-W. and Yong, S.-P.},
            isbn = {9781509051342},
        keywords = {Edge detection; Information science, Android platforms; Detection models; Facial landmark; Keypoints; Mobile vision; National Cheng-Kung University; System prototype; Web images, Face recognition},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010433076&doi=10.1109\%2fICCOINS.2016.7783286&partnerID=40&md5=49f25b88dbefab6ac0cccdc776dffc47},
        abstract = {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. {\^A}{\copyright} 2016 IEEE.}
}