Posture labeling based gesture classification for Turkish sign language using depth values

Ediz Saykol (Department of Computer Engineering, Beykent University, Istanbul, Turkey)
Halit Talha Türe (Department of Computer Engineering, Beykent University, Istanbul, Turkey)
Ahmet Mert Sirvanci (Department of Computer Engineering, Beykent University, Istanbul, Turkey)
Mert Turan (Department of Computer Engineering, Beykent University, Istanbul, Turkey)

Kybernetes

ISSN: 0368-492X

Publication date: 4 April 2016

Abstract

Purpose

The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL.

Design/methodology/approach

Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-based signing space clustering scheme is proposed to guide the feature extraction step. Gestures are then recognized by FSA that process temporally ordered posture labels.

Findings

Gesture classification accuracies and posture labeling performance are compared to k-nearest neighbor to show that the technique provides a reasonable framework for recognition of TSL gestures. A challenging set of gestures is tested, however the technique is extendible, and extending the training set will increase the performance.

Practical implications

The outcomes can be utilized as a system for educational purposes especially for visually impaired children. Besides, a communication system would be designed based on this framework.

Originality/value

The posture labeling scheme, which is inspired from keyframe labeling concept of video processing, is the original part of the proposed gesture classification framework. The search space is reduced to single dimension instead of 3D signing space, which also facilitates design of recognition schemes. Grid-based clustering scheme and location-based features are also new and depth values are received from Kinect. The paper is of interest for researchers in pattern recognition and computer vision.

Keywords

Citation

Saykol, E., Türe, H., Sirvanci, A. and Turan, M. (2016), "Posture labeling based gesture classification for Turkish sign language using depth values", Kybernetes, Vol. 45 No. 4, pp. 604-621. https://doi.org/10.1108/K-04-2015-0107

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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