Abstract
With the advancement in technology, we see that complex-valued data arise in many practical applications, specially in signal and image processing. In this paper, we introduce a new application by generating complex-valued dataset that represents various hand gestures in complex domain. The system consists of three components: real time hand tracking, hand-skeleton construction, and hand gesture recognition. A complex-valued neural network (CVNN) having one hidden layer and trained with Complex Levenberg-Marquardt (CLM) algorithm has been used to recognize 26 different gestures that represents English Alphabet. The result shows that the CLM provides reasonable recognition performance. In addition to that, a comparison among different activation functions have been presented.
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