Previous work in recognition of hand gestures has concentrated on classification of hand shapes, with relatively little work done on hand motions. This paper describes a recurrent neural network which has been trained to classify sixteen different hand trajectories, including relatively complex paths such as circles and backand-forth motions. The network's ability to anticipate the classificati...

3D gesture based systems are becoming ubiquitous and there are many mid-air hand gestures that exist for interacting with digital surfaces and displays. There is no well defined gesture set for 3D mid-air hand gestures which makes it difficult to develop applications that have consistent gestures. To understand what gestures exist we conducted the first comprehensive systematic literature revie...

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to appro...

Gesture recognition is an increasingly growing work area receiving, especially throughout the research fields of sign language recognition, security, human computer interaction, and intelligent cognitive systems. Human gesture is a spectrum that ranges from sign languages, through universal symbols, to natural and unconscious body moves. Gesture recognition techniques focus mainly on Sign langu...

Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for d...

This paper advances the methodology of hybrid classification architectures for face and hand gesture recognition tasks and shows their feasibility through experimental studies using the FERET data base and gesture images. The hybrid architecture, consisting of an ensemble of connectionist networks radial basis functions (RBF) and inductive decision trees (DT), combines the merits of 'holistic' ...

In this paper, a tele-operated gesture recognition mobile robot using a stereo vision is represented. We propose a real-time face and hand classification and 3D position extraction using a stereo vision embedded system. To obtain the disparity image, we used the stereo vision process on FPGA and developed the embedded system to obtain the image sequence for basic image processing. Then we appli...

Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. These ways are easy to result in complicated computation models, inconvenience of circuit connection and lower online recognition rate. Therefore it is imperative to have goo...

This survey presents a summary of the difficult field of static hand gesture recognition, that primarily consists of the popularity of well-defined signs supported a posture of the hand. Since human beings tend to differ in terms of size and shape the foremost difficult drawback consists of the segmentation and also the correct classification of the information’s gathered from the input image, ...