Orientation Histograms for Hand Gesture Recognition
We present a method to recognize hand gestures, based on a pattern recognition technique developed by McConnell [16] employing histograms of local orientation. We use the orientation histogram as a feature vector for gesture class cation and interpolation. This method is simple and fast to compute, and o ers some robustness to scene illumination changes. We have implemented a real-time version, which can distinguish a small vocabulary of about 10 di erent hand gestures. All the computation occurs on a workstation; special hardware is used only to digitize the image. A user can operate a computer graphic crane under hand gesture control, or play a game. We discuss limitations of this method. For moving or \dynamic gestures", the histogram of the spatio-temporal gradients of image intensity form the analogous feature vector and may be useful for dynamic gesture recognition. Reprinted from: IEEE Intl. Wkshp. on Automatic Face and Gesture Recognition, Zurich, June,