Gesture Recognition Applications Research Articles

Real-time gesture recognition system and application

In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate windows and objects within a graphical user interface. A hand segmentation procedure first extracts binary hand blob(s) from each frame of the acquired image sequence. Fourier descriptors are used to represent the shape of the hand blobs, and are input to radial-basis function (RBF) network(s) for pose classification. The pose likelihood vector from the RBF network output is used as input to the gesture recognizer, along with motion information. Gesture recognition performances using hidden Markov models (HMM) and recurrent neural networks (RNN) were investigated. Test results showed that the continuous HMM yielded the best performance with gesture recognition rates of 90.2%. Experiments with combining the continuous HMMs and RNNs revealed that a linear combination of the two classifiers improved the classification results to 91.9%. The gesture recognition system was deployed in a prototype user interface application, and users who tested it found the gestures intuitive and the application easy to use. Real time processing rates of up to 22 frames per second were obtained.

Visual interpretation of hand gestures for human-computer interaction: a review

The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). In particular, visual interpretation of hand gestures can help in achieving the ease and naturalness desired for HCI. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. We survey the literature on visual interpretation of hand gestures in the context of its role in HCI. This discussion is organized on the basis of the method used for modeling, analyzing, and recognizing gestures. Important differences in the gesture interpretation approaches arise depending on whether a 3D model of the human hand or an image appearance model of the human hand is used. 3D hand models offer a way of more elaborate modeling of hand gestures but lead to computational hurdles that have not been overcome given the real-time requirements of HCI. Appearance-based models lead to computationally efficient purposive approaches that work well under constrained situations but seem to lack the generality desirable for HCI. We also discuss implemented gestural systems as well as other potential applications of vision-based gesture recognition. Although the current progress is encouraging, further theoretical as well as computational advances are needed before gestures can be widely used for HCI. We discuss directions of future research in gesture recognition, including its integration with other natural modes of human-computer interaction.