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.
@article{arxiv.2004.01225,
title = {Temporal Accumulative Features for Sign Language Recognition},
author = {Ahmet Alp Kındıroğlu and Oğulcan Özdemir and Lale Akarun},
journal= {arXiv preprint arXiv:2004.01225},
year = {2020}
}