TITLE:
Vision-Based Hand Gesture Spotting and Recognition Using CRF and SVM
AUTHORS:
Fayed F. M. Ghaleb, Ebrahim A. Youness, Mahmoud Elmezain, Fatma Sh. Dewdar
KEYWORDS:
Human Computer Interaction, Conditional Random Fields, Support Vector Machine, Elliptic Fourier, Zernike Moments
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.7,
July
24,
2015
ABSTRACT: 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.