Eye and mouth state detection algorithm based on contour feature extraction

Yingyu Ji; Shigang Wang; Yang Lu; Jian Wei; Yan Zhao

doi:10.1117/1.JEI.27.5.051205

9 February 2018 Eye and mouth state detection algorithm based on contour feature extraction

Yingyu Ji, Shigang Wang, Yang Lu, Jian Wei, Yan Zhao

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Journal of Electronic Imaging, Vol. 27, Issue 5, 051205 (February 2018). https://doi.org/10.1117/1.JEI.27.5.051205

Abstract

Eye and mouth state analysis is an important step in fatigue detection. An algorithm that analyzes the state of the eye and mouth by extracting contour features is proposed. First, the face area is detected in the acquired image database. Then, the eyes are located by an EyeMap algorithm through a clustering method to extract the sclera-fitting eye contour and calculate the contour aspect ratio. In addition, an effective algorithm is proposed to solve the problem of contour fitting when the human eye is affected by strabismus. Meanwhile, the value of chromatism s is defined in the RGB space, and the mouth is accurately located through lip segmentation. Based on the color difference of the lip, skin, and internal mouth, the internal mouth contour can be fitted to analyze the opening state of mouth; at the same time, another unique and effective yawning judgment mechanism is considered to determine whether the driver is tired. This paper is based on the three different databases to evaluate the performance of the proposed algorithm, and it does not need training with high calculation efficiency.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Yingyu Ji, Shigang Wang, Yang Lu, Jian Wei, and Yan Zhao "Eye and mouth state detection algorithm based on contour feature extraction," Journal of Electronic Imaging 27(5), 051205 (9 February 2018). https://doi.org/10.1117/1.JEI.27.5.051205

Received: 7 September 2017; Accepted: 16 January 2018; Published: 9 February 2018

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