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In this paper, we propose a set of features called temporal accumulativefeatures (TAF) for representing and recognizing isolated sign languagegestures. By incorporating sign language specific constructs to betterrepresent the unique linguistic characteristic of sign language videos, we havedevised an efficient and fast SLR method for recognizing isolated sign languagegestures. The proposed method is an HSV based accumulative video representationwhere keyframes based on the linguistic movement-hold model are represented bydifferent colors. We also incorporate hand shape information and using a smallscale convolutional neural network, demonstrate that sequential modeling ofaccumulative features for linguistic subunits improves upon baselineclassification results.

Temporal Accumulative Features for Sign Language Recognition