  Human-to-Robot handovers are useful for many Human-Robot Interaction
scenarios. It is important to recognize when a human intends to initiate
handovers, so that the robot does not try to take objects from humans when a
handover is not intended. We pose the handover gesture recognition as a binary
classification problem in a single RGB image. Three separate neural network
modules for detecting the object, human body key points and head orientation,
are implemented to extract relevant features from the RGB images, and then the
feature vectors are passed into a deep neural net to perform binary
classification. Our results show that the handover gestures are correctly
identified with an accuracy of over 90%. The abstraction of the features makes
our approach modular and generalizable to different objects and human body
types.
