HMM-based continuous sign language recognition using a fast optical flow parameterization of visual information

This paper presents a preliminary study of an optical flow-based parameterization of visual information in a sign language recognition system using Hidden Markov Models (HMM). Current feature extraction processes need initialization, tracking and segmentation stages in order to describe signer gestures. Our aim is to develop a single and fast technique to reduce computational complexity which doesn't require these stages and is able to work in mobile devices with limited hardware resources. The Moving Block Distance (MBD) parameterization is an interesting first approach for this purpose, proved by two signers under a static background constraint. A lexicon of 33 basic word units (signemes) was used to build the data set containing phrases with a variable number of words. Continuous recognition results achieve more than 99% accuracy in close test. Index Terms: multi-modal recognition, visual feature extraction, mobile devices, sign language, optical flow.