Hand gesture recognition and spotting in uncontrolled environments based on classifier weighting

Pure appearance based Hand Gesture Recognition and Spotting in uncontrolled environments are challenging tasks due to the uncontrolled scene settings include: multiple hand regions in the scene; background moving objects; scale, speed and location variations of the gesture trajectories; changing lighting conditions and frontal occlusions. An appearance based method based on a novel classifier weighting scheme is proposed in this paper for hand gesture recognition and spotting in uncontrolled environments. The method is capable of producing decent performance with the presence of all the aforementioned challenges. Two databases are used for evaluating the proposed method, the Palm Graffiti Digits Database and the Warwick Hand Gesture Database. The experimental results demonstrate that the proposed method can deal with the challenges from uncontrolled environments without any prior knowledge and enhance the performance of the initial classifier.