  User's intentions may be expressed through spontaneous gesturing, which have
been seen only a few times or never before. Recognizing such gestures involves
one shot gesture learning. While most research has focused on the recognition
of the gestures itself, recently new approaches were proposed to deal with
gesture perception and production as part of the same problem. The framework
presented in this work focuses on learning the process that leads to gesture
generation, rather than mining the gesture's associated features. This is
achieved using kinematic, cognitive and biomechanic characteristics of human
interaction. These factors enable the artificial production of realistic
gesture samples originated from a single observation. The generated samples are
then used as training sets for different state-of-the-art classifiers.
Performance is obtained first, by observing the machines' gesture recognition
percentages. Then, performance is computed by the human recognition from
gestures performed by robots. Based on these two scenarios, a composite new
metric of coherency is proposed relating to the amount of agreement between
these two conditions. Experimental results provide an average recognition
performance of 89.2% for the trained classifiers and 92.5% for the
participants. Coherency in recognition was determined at 93.6%. While this new
metric is not directly comparable to raw accuracy or other pure
performance-based standard metrics, it provides a quantifier for validating how
realistic the machine generated samples are and how accurate the resulting
mimicry is.
