Hand Pose Recognition by using Masked Zernike Moments

In this paper we present a novel way of applying Zernike moments for image matching. Zernike moments are obtained from projecting image information under a circumscribed circle to Zernike basis function. However, the problem is that the power of discrimination may be reduced because hand images include lots of overlapped information due to their shape characteristic. On the other hand, in the pose discrimination shape information of hands excluding the overlapped area can increase the power of discrimination. In order to solve the overlapped information problem, we present a way of applying subtraction masks. Internal mask R1 eliminates overlapped information in hand images, while external mask R2 weighs outstanding features of hand images. Mask R3 combines the results from the image masked by R1 and the image masked by R2. The moments obtained by R3 mask increase the accuracy of discrimination for hand poses, which is shown in experiments by comparing conventional methods.