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.
Highlights
IntroductionAn ideal recognizer will extract gesture segments from the input signal, and match them with reference patterns regardless of the spatio-temporal variabilities
The task of locating the start and the end points that correspond to a gesture of interest is a challenging task in Human Computer Interaction
On a standard desktop PC, training process is more expensive for conditional random field (CRF) since the time which the model needs ranges from 20 minutes to several hours based on observation window
Summary
An ideal recognizer will extract gesture segments from the input signal, and match them with reference patterns regardless of the spatio-temporal variabilities. [8] propose a novel method for designing threshold models in a conditional random field (CRF) model which performs an adaptive threshold for distinguishing between signs in a vocabulary and non-sign patterns. These methods have the following consequent drawback to detect the reliable end point of a gesture and find the start point by back-tracking. To face the mentioned challenges, CRF forward gesture spotting by using Circular Buffer method is proposed, which simultaneously handles the hand gesture spotting and recognition in stereo color image sequences without time delay. Our experiments on own dataset, showed that the proposed approach is more robust and yields promising results when comparing favorably with those previously reported throughout the literature
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