Cascaded hybrid Wavelet Network for hand gestures recognition

This paper presents a new cascaded hybrid Wavelet Network Classifier (CHWNC) designed for hand gesture recognition in real time applications. This paper contains two key contributions. The first is the amelioration of our previous works in the classification domain employing wavelet networks (WN). Precisely, by ameliorating the training way of the latest wavelet network classifier (WNC) version by representing each training class by one WN instead of creating a WN for each training image. This contribution makes very rapid the test phase by reducing the number of comparisons between test images WNs and training WNs. The second contribution is the proposition of a new wavelet network architecture including the cascade notion which decomposes the WN on a set of stages. The new architecture has as aim not only to make recognitions robust and rapid but also to reject as fast as possible gestures which must not be considered by the system (spontaneous gestures). Experiments, based on a well known hand posture dataset, show that our method is very robust and rapid compared to already existing ones.