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Sensors 2017, 17(7), 1534; https://doi.org/10.3390/s17071534

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
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Received: 2 June 2017 / Revised: 26 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Abstract

The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods. View Full-Text
Keywords: classification of open and closed eyes; eye status tracking-based driver drowsiness detection; visible light camera; deep residual convolutional neural network classification of open and closed eyes; eye status tracking-based driver drowsiness detection; visible light camera; deep residual convolutional neural network
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Kim, K.W.; Hong, H.G.; Nam, G.P.; Park, K.R. A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor. Sensors 2017, 17, 1534.

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