1. A method of identifying an observed bimanual hand gesture, the method comprising: tracking movement of a first hand during an observed bimanual gesture; tracking movement of a second hand during the observed bimanual gesture; determining a first set of measures of likelihoods that the tracked movement of the first hand corresponds to one or more known bimanual gestures; determining a second set of measures of likelihoods that the tracked movement of the second hand corresponds to the one or more known bimanual gestures; and based on the first set of measures of the likelihoods that the tracked movement of the first hand corresponds to the one or more known bimanual gestures and the second set of measures of the likelihoods that the tracked movement of the second hand corresponds to the one or more known bimanual gestures, identifying, by a computing device that includes hardware, a particular one of the one or more known bimanual gestures as corresponding to the observed bimanual gesture, wherein tracking movement of a first hand during an observed bimanual gesture comprises extracting a sequence of images of the first hand from a sequence of images of the observed bimanual gesture; wherein tracking movement of a second hand during an observed bimanual gesture comprises extracting a sequence of images of the second hand from the sequence of images of the observed bimanual gesture; wherein determining a first set of measures of likelihoods that the tracked movement of the first hand corresponds to one or more known bimanual gestures comprises: identifying a sequence of principal components corresponding to the sequence of images of the first hand, and based on the identified sequence of principal components corresponding to the sequence of images of the first hand, using trained Hidden Markov Models that model first-hand movements of the one or more known bimanual gestures to determine the first set of measures of likelihoods that the tracked movement of the first hand corresponds to the one or more known bimanual gestures; and wherein determining a second set of measures of likelihoods that the tracked movement of the second hand corresponds to one or more known bimanual gestures comprises: identifying a sequence of principal components corresponding to the sequence of images of the second hand, and based on the identified sequence of principal components corresponding to the sequence of images of the second hand, using trained Hidden Markov Models that model second-hand movements of the one or more known bimanual gestures to determine the second set of measures of likelihoods that the tracked movement of the second hand corresponds to the one or more known bimanual gestures.