GESTURE RECOGNITION USING A NMF-BASED REPRESENTATION OF MOTION-TRACES EXTRACTED FROM DEPTH SILHOUETTES

We present a novel approach that classifies full-body human gestures using original spatio-temporal features obtained by applying non-negative matrix factorisation (NMF) to an extended depth silhouette representation. This extended representation, the motion-trace representation, incorporates temporal dimensions as it is built by superimposition of consecutive depth silhouettes. From this representation, a dictionary of local motion features is learned using NMF. Thus the projection of these local motion feature components on the incoming motion-traces results in a compact spatio-temporal feature representation. Those new features are then exploited using hidden Markov models for gesture recognition. Our experiments on a gesture dataset show that our approach outperforms more traditional methods that use pose features or decomposition techniques such as principal component analysis.