Two User Adaptation-Derived Features for Biometrical Classifications of User Identity in 3D-Sensor-Based Body Gesture Recognition Applications

For human gesture recognition applications, 3D-sensor-based approaches have received considerable attention and are crucial for future applications of an advanced body sensor network (BSN). A practical gesture recognition system is to apply active gesture patterns of a gesture-making user for body action classifications. 3D-sensor-based gesture recognition, categorized as biometric recognition in BSN applications, is greatly lack of the extensible cognition ability due to substandard recognition accuracy on the gesture-making user identity. To overcome this problem, this paper proposes an active gesture-based user identity recognition approach using a robust feature design, called user adaptation (UA) features, derived from a UA process. Two different UA features, namely Eigen Centroid-UA and Eigen Transform-UA features, were developed in this paper to accurately represent the adaptive learning tendency of gesture recognition for a specific gesture-making user. Compared with traditional 3D sensor gesture-based identity recognition approaches that employ only the feature of fixed body skeleton information without any UA designs, the presented UA-feature can exhibit fine adaptation learning continuously to the specific action user; therefore, superior identity recognition accuracy will be constantly ensured. To demonstrate the efficiency and effectiveness of the developed robust UA features in this paper, experiments on gesture-making user identification by Gaussian mixture model applying the proposed Eigen Centroid-UA feature and verification by support vector machine applying the proposed Eigen Transform-UA feature were conducted.