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Proceedings Paper

Classification of hand over face gestures using deep learning
Author(s): El Sayed A. Sharara; A. Tsuji; K. Terada
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Paper Abstract

Facial expressions and hand gestures are recognized as a part of human emotions especially in a feeling of fatigue signs. In computer vision research, a positioning of hand over face is one of the challenging problem caused by difficulty of the difference of skin color for hands and face. In this paper, we present a method for classifying six positions of the hand over face which is able to identify the signs of feeling fatigue for the visual display terminal (VDT) workers. We apply a deep learning method in order to compare with the methods used the face and skin colors detection, processing the edge detection and feature extraction algorithms. In addition, GoogleNet is used for training a data set made in the simulated VDT workers environment. The data set includes 1,440 images, the participants from several countries, Egypt, Japan, Bangladesh, Mongolia and Rwanda to cover a wide range of skin tones. The data set is categorized by six groups of the positions of hand over face. These groups consist of hands on a forehead, eyes, nose, mouth, right and left face. The experiments were performed using MATLAB to implement our proposed method. The system achieved average recognition ratio 99.3 % in all hand over face gestures.

Paper Details

Date Published: 16 July 2019
PDF: 6 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117219 (16 July 2019); doi: 10.1117/12.2521269
Show Author Affiliations
El Sayed A. Sharara, The Univ. of Tokushima (Japan)
A. Tsuji, The Univ. of Tokushima (Japan)
K. Terada, The Univ. of Tokushima (Japan)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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