Classification of hand over face gestures using deep learning

El Sayed A. Sharara; A. Tsuji; K. Terada

doi:10.1117/12.2521269

16 July 2019 Classification of hand over face gestures using deep learning

El Sayed A. Sharara, A. Tsuji, K. Terada

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 1117219 (2019) https://doi.org/10.1117/12.2521269
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

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.

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El Sayed A. Sharara, A. Tsuji, and K. Terada "Classification of hand over face gestures using deep learning", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 1117219 (16 July 2019); https://doi.org/10.1117/12.2521269

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KEYWORDS

Facial recognition systems

Skin

Image classification

Nose

Detection and tracking algorithms

Process control

Convolutional neural networks

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