  In this paper, we propose a set of features called temporal accumulative
features (TAF) for representing and recognizing isolated sign language
gestures. By incorporating sign language specific constructs to better
represent the unique linguistic characteristic of sign language videos, we have
devised an efficient and fast SLR method for recognizing isolated sign language
gestures. The proposed method is an HSV based accumulative video representation
where keyframes based on the linguistic movement-hold model are represented by
different colors. We also incorporate hand shape information and using a small
scale convolutional neural network, demonstrate that sequential modeling of
accumulative features for linguistic subunits improves upon baseline
classification results.
