Elmezain, M., Al-Hamadi, A. and Michaelis, B. (2010) Robust Methods for Hand Gesture Spotting and Recognition Using Hidden Markov Models and Conditional Random Fields. IEEE Symposium on Signal Processing and Information Technology (ISSPIT), Luxor, 15-18 December 2010, 131-136.
http://dx.doi.org/10.1109/ISSPIT.2010.5711749

has been cited by the following article:

• 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.