Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC)
Metadata Last Update2020: (UTC) administrator
Citation KeyRodríguezCahuAraúChav:2015:FiSpRe
TitleFinger Spelling Recognition using Kernel Descriptors and Depth Images
Access Date2022, Jan. 19
Number of Files1
Size1597 KiB
Context area
Author1 Rodríguez, Karla Catherine Otiniano
2 Cahuina, Edward Cayllahua
3 Araújo, Arnaldo de Albuquerque
4 Chavez, Guillermo Cámara
Affiliation1 Federal University of Minas Gerais
2 Federal University of Minas Gerais
3 Federal University of Minas Gerais
4 Federal University of Ouro Preto
EditorPapa, João Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-24 23:30:24 :: -> administrator ::
2020-02-19 02:14:04 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsfinger spelling
depth images
kernel descriptors
AbstractDeaf people use systems of communication based on sign language and finger spelling. Finger spelling is a system where each letter of the alphabet is represented by a unique and discrete movement of the hand. RGB and depth images can be used to characterize hand shapes corresponding to letters of the alphabet. There exists an advantage of depth sensors, as Kinect, over color cameras for finger spelling recognition: depth images provide 3D information of the hand. In this paper, we propose a model for finger spelling recognition based on depth information using kernel descriptors, consisting of four stages. The performance of this approach is evaluated on a dataset of real images of the American Sign Language finger spelling. Different experiments were performed using a combination of both descriptors over depth information. Our approach obtains 92.92% of mean accuracy with 50% of samples for training; outperforming other state-of-the-art methods. > SDLA > SIBGRAPI 2015 > Finger Spelling Recognition...
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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