Gesture Recognition Using Image Comparison Methods

Abstract. We introduce the use of appearance-based features, and tangent distance or the image distortion model to account for image variability within the hidden Markov model emission probabilities to recognize gestures. No tracking, segmentation of the hand or shape models have to be defined. The distance measures also perform well for template matching classifiers. We obtain promising first results on a new database with the German finger-spelling alphabet. This newly recorded database is freely available for further research.

• Type: Conference
• Year: 2005
• Where: GW
• Authors: Philippe Dreuw, Daniel Keysers, Thomas Deselaers, Hermann Ney
• Keywords: GW 2005, Image Distortion Model, Model Emission Probabilities, Tangent Distance