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Sensors 2018, 18(8), 2577; https://doi.org/10.3390/s18082577

Handshape Recognition Using Skeletal Data

Department of Computer and Control Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland
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Received: 10 July 2018 / Revised: 2 August 2018 / Accepted: 5 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Visual Sensors)
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Abstract

In this paper, a method of handshapes recognition based on skeletal data is described. A new feature vector is proposed. It encodes the relative differences between vectors associated with the pointing directions of the particular fingers and the palm normal. Different classifiers are tested on the demanding dataset, containing 48 handshapes performed 500 times by five users. Two different sensor configurations and significant variation in the hand rotation are considered. The late fusion at the decision level of individual models, as well as a comparative study carried out on a publicly available dataset, are also included. View Full-Text
Keywords: handshape recognition; sign language; finger alphabet; skeletal data handshape recognition; sign language; finger alphabet; skeletal data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kapuscinski, T.; Organisciak, P. Handshape Recognition Using Skeletal Data. Sensors 2018, 18, 2577.

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