Hand gesture recognition using ensembles of radial basis function (RBF) networks and decision trees

Hand gestures are the natural form of communication among people, yet human-computer interaction is still limited to mice movements. The use of hand gestures in the field of human-computer interaction has attracted renewed interest in the past several years. Special glove-based devices have been developed to analyze finger and hand motion and use them to manipulate and explore virtual worlds. To further enrich the naturalness of the interaction, different computer vision-based techniques have been developed. At the same time the need for more efficient systems has resulted in new gesture recognition approaches. In this paper we present an hybrid intelligent system for hand gesture recognition. The hybrid approach consists of an ensemble of connectionist networks radial basis functions (RBF) - and inductive decision trees (AQDT). Cross Validation (CV) experimental results yield a false negative rate of 1.7% and a false positive rate of 1% while the evaluation takes place on a data base including 150 images corresponding to 15 gestures of 5 subjects. In order to assess the robustness of the system, the vocabulary of the gestures has been increased from 15 to 25 and the size of the database from 150 to 750 images corresponding now to 15 subjects. Cross Validation (CV) experimental results yield a false negative rate of 3.6% and a false positive rate of 1.8% respectively. The benefits of our hybrid architecture include (i) robustness via query by consensus as provided by ensembles of networks when facing the inherent variability of the image formation and data acquisition process, (ii) classifications made using decision trees, (iii) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds and (iv) interpretability of the way classification and retrieval is eventually achieved.