%0 Conference Paper %1 lahiani2015gesture %A Lahiani, Houssem %A Elleuch, Mohamed %A Kherallah, Monji %B 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA) %D 2015 %E Abraham, Ajith %E Alimi, Adel M. %E Haqiq, Abdelkrim %E Orozco-Barbosa, Luis %E Amar, Chokri Ben %E Berqia, Amine %E Halima, Mohamed Ben %E Muda, Azah Kamilah %E Ma, Kun %I IEEE %K android gesture-recognition human-computer-interactio image-segmentation real support-vector-machine %P 591-596 %R 10.1109/ISDA.2015.7489184 %T Real time hand gesture recognition system for android devices %U https://ieeexplore.ieee.org/document/7489184 %X Hand gestures are natural and intuitive communication way for the human being to interact with his environment. They serve to designate or manipulate objects, to enhance speech, or communicate in a noisy place. They can also be a separate language. Gestures can have different meanings according to the language or culture. They can also be a way to interact with machines. The subject of our research concerns the design and development of computer vision methods for recognizing hand gestures by a mobile device. We have proposed a system based on SVM for recognizing various hand gestures. The system consists of four steps: hand segmentation, smoothing, feature extraction and classification. The idea here is to allow the smartphone to perform all necessary steps to recognize gestures without the need to connect to a computer in which a database is located to perform training process. With this system, all steps can be done by the smartphone. In this paper, for image acquisition, frontal camera of the smartphone is used. After that frames are gotten from the video, the color sampling is done which is followed by making binary representation of the hand, and then contours representing the hand were described with convex polygons to get information about fingertips and finally the input gesture was recognized using proper classifier.

@inproceedings{lahiani2015gesture, abstract = {Hand gestures are natural and intuitive communication way for the human being to interact with his environment. They serve to designate or manipulate objects, to enhance speech, or communicate in a noisy place. They can also be a separate language. Gestures can have different meanings according to the language or culture. They can also be a way to interact with machines. The subject of our research concerns the design and development of computer vision methods for recognizing hand gestures by a mobile device. We have proposed a system based on SVM for recognizing various hand gestures. The system consists of four steps: hand segmentation, smoothing, feature extraction and classification. The idea here is to allow the smartphone to perform all necessary steps to recognize gestures without the need to connect to a computer in which a database is located to perform training process. With this system, all steps can be done by the smartphone. In this paper, for image acquisition, frontal camera of the smartphone is used. After that frames are gotten from the video, the color sampling is done which is followed by making binary representation of the hand, and then contours representing the hand were described with convex polygons to get information about fingertips and finally the input gesture was recognized using proper classifier.}, added-at = {2019-11-16T02:29:42.000+0100}, author = {Lahiani, Houssem and Elleuch, Mohamed and Kherallah, Monji}, biburl = {https://www.bibsonomy.org/bibtex/29c14ea6755537ee1097e476e4d4f426f/jpmor}, booktitle = {2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)}, description = {Real time hand gesture recognition system for android devices - IEEE Conference Publication}, doi = {10.1109/ISDA.2015.7489184}, editor = {Abraham, Ajith and Alimi, Adel M. and Haqiq, Abdelkrim and Orozco-Barbosa, Luis and Amar, Chokri Ben and Berqia, Amine and Halima, Mohamed Ben and Muda, Azah Kamilah and Ma, Kun}, interhash = {1dfaaefb44175e73991b8e71baabd0ed}, intrahash = {9c14ea6755537ee1097e476e4d4f426f}, keywords = {android gesture-recognition human-computer-interactio image-segmentation real support-vector-machine}, language = {English}, month = dec, pages = {591-596}, publisher = {IEEE}, school = {University of Sfax}, timestamp = {2020-10-07T13:36:50.000+0200}, title = {Real time hand gesture recognition system for android devices}, url = {https://ieeexplore.ieee.org/document/7489184}, year = 2015 }