Depth-based hand pose recognizer using learning vector quantization

Abstract

The paper describes a depth-based hand pose recognizer by means of a Learning Vector Quantization (LVQ) classifier. The hand pose recognizer is composed of three modules. The first module segments the scene isolating the hand. The second one carries out the feature extraction, representing the hand by a set of 8 features. The third module, the classifier, is a LVQ. The recognizer, tested on a dataset of 6500 hand poses, carried out by people of different sex and physical aspect, has shown an accuracy larger than 99% recognition rate. The hand pose recognizer accuracy is among highest presented in literature for hand pose recognition.