  We develop Few-Shot Learning models trained to recognize five or ten
different dynamic hand gestures, respectively, which are arbitrarily
interchangeable by providing the model with one, two, or five examples per hand
gesture. All models were built in the Few-Shot Learning architecture of the
Relation Network (RN), in which Long-Short-Term Memory cells form the backbone.
The models use hand reference points extracted from RGB-video sequences of the
Jester dataset which was modified to contain 190 different types of hand
gestures. Result show accuracy of up to 88.8% for recognition of five and up to
81.2% for ten dynamic hand gestures. The research also sheds light on the
potential effort savings of using a Few-Shot Learning approach instead of a
traditional Deep Learning approach to detect dynamic hand gestures. Savings
were defined as the number of additional observations required when a Deep
Learning model is trained on new hand gestures instead of a Few Shot Learning
model. The difference with respect to the total number of observations required
to achieve approximately the same accuracy indicates potential savings of up to
630 observations for five and up to 1260 observations for ten hand gestures to
be recognized. Since labeling video recordings of hand gestures implies
significant effort, these savings can be considered substantial.
