Journal
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW)
Volume -, Issue -, Pages 1288-1297Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICCVW.2019.00164
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Funding
- Turkish ministry of development under the TAM Project [2007K120610]
- TUBITAK Project [117E059]
- Bogazici Uni. BAP Project [14504]
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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 SIR 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.
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