Human gait is an attractive modality for recognizing people at a distance. Gait recognition systems aims to identify people by studying their manner of walking. In this paper, we contribute by the creation of a new approach for gait recognition based on fast wavelet network (FWN) classifier. To guaranty the effectiveness of our gait recognizer, we have employed both static and dynamic gait characteristics. So, to extract the static features (dimension of the body part), model based method was employed. Thus, for the dynamic features (silhouette appearance and motion), model free method was used. The combination of these two methods aims at strengthens the WN classification results. Experimental results employing universal datasets show that our new gait recognizer performs better than already established ones.