Perception of drowsiness based on correlation with facial image features

Yugo Sato; Takuya Kato; Naoki Nozawa; Shigeo Morishima

doi:10.1145/2931002.2947705

Perception of drowsiness based on correlation with facial image features

Yugo Sato, Takuya Kato, Naoki Nozawa, Shigeo Morishima

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.

Original language	English
Title of host publication	Proceedings of the ACM Symposium on Applied Perception, SAP 2016
Publisher	Association for Computing Machinery, Inc
Pages	139
Number of pages	1
ISBN (Electronic)	9781450343831
DOIs	https://doi.org/10.1145/2931002.2947705
Publication status	Published - 2016 Jul 22
Event	ACM Symposium on Applied Perception, SAP 2016 - Anaheim, United States Duration: 2016 Jul 22 → 2016 Jul 23

Publication series

Name	Proceedings of the ACM Symposium on Applied Perception, SAP 2016

Other

Other	ACM Symposium on Applied Perception, SAP 2016
Country/Territory	United States
City	Anaheim
Period	16/7/22 → 16/7/23

Keywords

Correlation coefficient
Drowsiness detection
Face evaluation
Feature learning
K-nearest neighbor algorithm

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Applied Mathematics
Theoretical Computer Science

Access to Document

10.1145/2931002.2947705

Cite this

Perception of drowsiness based on correlation with facial image features. / Sato, Yugo; Kato, Takuya; Nozawa, Naoki et al.
Proceedings of the ACM Symposium on Applied Perception, SAP 2016. Association for Computing Machinery, Inc, 2016. p. 139 (Proceedings of the ACM Symposium on Applied Perception, SAP 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sato, Y, Kato, T, Nozawa, N & Morishima, S 2016, Perception of drowsiness based on correlation with facial image features. in Proceedings of the ACM Symposium on Applied Perception, SAP 2016. Proceedings of the ACM Symposium on Applied Perception, SAP 2016, Association for Computing Machinery, Inc, pp. 139, ACM Symposium on Applied Perception, SAP 2016, Anaheim, United States, 16/7/22. https://doi.org/10.1145/2931002.2947705

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abstract = "This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.",

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AU - Nozawa, Naoki

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Y1 - 2016/7/22

N2 - This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.

AB - This paper presents a video-based method for detecting drowsiness. Generally, human beings can perceive their fatigue and drowsiness through looking at faces. The ability to perceive the fatigue and the drowsiness has been studied in many ways. The drowsiness detection method based on facial videos has been proposed [Nakamura et al. 2014]. In their method, a set of the facial features calculated with the Computer Vision techniques and the k-nearest neighbor algorithm are applied to classify drowsiness degree. However, the facial features that are ineffective against reproducing the perception of human beings with the machine learning method are not removed. This factor can decrease the detection accuracy.

KW - Correlation coefficient

KW - Drowsiness detection

KW - Face evaluation

KW - Feature learning

KW - K-nearest neighbor algorithm

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Perception of drowsiness based on correlation with facial image features

Abstract

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Keywords

ASJC Scopus subject areas

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