Static hand gesture recognition using discriminative 2D Zernike moments

Hand gesture recognition plays a vital role in developing vision-based communication for human-computer interaction. This paper presents a novel static hand gesture recognition method using the two dimensional Zernike moments (2D ZMs) those are considered as effective features when patterns in images possess distortions due to rotation, scaling or viewing angle. The key contribution of this paper lies in the fact that a discriminative set of ZMs are used to represent features of the hand postures as opposed to traditional features obtained from heuristic choice of fixed-order moments. The orthogonal nature of the 2D ZMs allows the estimation of the discrimination power of the individual moments by using the inter- and intra-class variances of the features. The nearest neighbor classifier is employed on the discriminative ZMs (DZMs) to recognize the hand postures in a computationally efficient way. Experimental results on commonly-referred database show that the proposed DZM-based method provides recognition accuracies better than that provided by the conventional principal component analysis, Fourier descriptor or existing ZM-based methods.