Event-based hand shadow recognition with varied light intensity and background subtraction

In this paper, we propose the use of event-based
hand shadow images for the hand gesture recognition problem
and we aim at a portable deep learning shadow-based detection
application. Such an interaction-based application requires fast
sensing, and limited data transmission (since classification is
performed at the edge or back-end server). In addition, it needs
to easily adapt to different testing environments with varied
lighting conditions and environment backgrounds. In order to
overcome these limitations, we introduce an image pre-processing
step based on special features of event-based cameras, which
reduces the information contained in ’gray-scale’ hand shadow
images that are needed for classifying gestures, and at the same
time reduces the impact of light illuminations and background
interference while maintaining a high classification accuracy.