Discriminative training of hidden Markov Models for gesture recognition

As homes and workplaces become increasingly automated, an efficient, inclusive and language-independent human-computer interaction mechanism will become more necessary. Isolated gesture recognition can be used to this end. Gesture recognition is a problem of modelling temporal data. Non-temporal models can be used for gesture recognition, but require that the signals be adapted to the models. For example, the requirement of fixed-length inputs for support-vector machine classification. Hidden Markov models are probabilistic graphical models that were designed to operate on time-series data, and are sequence length invariant. However, in traditional hidden Markov modelling, models are trained via the maximum likelihood criterion and cannot perform as well as a discriminative classifier. This study employs minimum classification error training to produce a discriminative HMM classifier. The classifier is then applied to an isolated gesture recognition problem, using skeletal features. The Montalbano gesture dataset is used to evaluate the system on the skeletal modality alone. This positions the problem as one of fine-grained dynamic gesture recognition, as the hand pose information contained in other modalities are ignored. The method achieves a highest accuracy of 87.3%, comparable to other results reported on the Montalbano dataset using discriminative non-temporal methods. The research will show that discriminative hidden Markov models can be used successfully as a solution to the problem of isolated gesture recognitio