Issue No. 11 - November (2008 vol. 30)
Jeroen F. Lichtenauer , Delft University of Technology, Delft
Emile A. Hendriks , Delft University of Technology, Delft
Marcel J.T. Reinders , Delft University of Technology, Delft
To recognize speech, handwriting or sign language, many hybrid approaches have been proposed that combine Dynamic Time Warping (DTW) or Hidden Markov Models (HMM) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated because of conflicting likelihood modelling demands. To overcome these restrictions, we propose to use Statistical DTW (SDTW) only for time warping, while classifying the warped features with a different method. Two novel statistical classifiers are proposed (CDFD and Q-DFFM), both using a selection of discriminative features (DF), and are shown to outperform HMM and SDTW. However, we have found that combining likelihoods of multiple models in a second classification stage degrades performance of the proposed classifiers, while improving performance with HMM and SDTW. A proof-of-concept experiment, combining DFFM mappings of multiple SDTW models with SDTW likelihoods, shows that also for model-combining, hybrid classification can provide significant improvement over SDTW. Although recognition is mainly based on 3D hand motion features, these results can be expected to generalize to recognition with more detailed measurements such as hand/body pose and facial expression.
Time series analysis, Face and gesture recognition, Markov processes, Classifier design and evaluation, Real-time systems, 3D/stereo scene analysis, Vision and Scene Understanding, Artificial Intelligence, Computing Methodology
J. F. Lichtenauer, E. A. Hendriks and M. J. Reinders, "Sign Language Recognition by Combining Statistical DTW and Independent Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 2040-2046, 2008.