Learning to recognize a sign from a single example

We present a method to automatically construct a sign language classifier for a previously unseen sign. The only required input of a new sign is one example, performed by a sign language tutor. The method works by comparing the measurements of the new sign to signs that have been trained on a large number of persons. The parameters of the respective trained classifier models are used to construct a classification model for the new sign. We show that the performance of a classifier constructed from an instructed sign is significantly better than that of dynamic time warping (DTW) with the same sign. Using only a single example, the proposed method has a performance comparable to a regular training with five examples, while being more stable because of the larger source of information.