Vision-Based Hand Gesture Spotting and Recognition Using CRF and SVM

Autor:	Ebrahim A. Youness, Fatma Sh. Dewdar, Mahmoud Elmezain, Fayed F. M. Ghaleb
Rok vydání:	2015
Předmět:	Conditional random field Computer science business.industry Zernike polynomials Support vector machine symbols.namesake Fourier transform Discriminative model Depth map symbols Computer vision Artificial intelligence Invariant (mathematics) business Gesture
Zdroj:	Journal of Software Engineering and Applications. :313-323
ISSN:	1945-3124 1945-3116
Popis:	In this paper, a novel gesture spotting and recognition technique is proposed to handle hand gesture from continuous hand motion based on Conditional Random Fields in conjunction with Support Vector Machine. Firstly, YCbCr color space and 3D depth map are used to detect and segment the hand. The depth map is to neutralize complex background sense. Secondly, 3D spatio-temporal features for hand volume of dynamic affine-invariants like elliptic Fourier and Zernike moments are extracted, in addition to three orientations motion features. Finally, the hand gesture is spotted and recognized by using the discriminative Conditional Random Fields Model. Accordingly, a Support Vector Machine verifies the hand shape at the start and the end point of meaningful gesture, which enforces vigorous view invariant task. Experiments demonstrate that the proposed method can successfully spot and recognize hand gesture from continuous hand motion data with 92.50% recognition rate.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e35c748d4f4b88324b2e0eb232235994 https://doi.org/10.4236/jsea.2015.87032 Zobrazit plný text záznamu