abstract

Perception of drowsiness based on correlation with facial image features

Authors:

Yugo Sato,

Takuya Kato,

Naoki Nozawa,

Shigeo MorishimaAuthors Info & Claims

SAP '16: Proceedings of the ACM Symposium on Applied Perception

Page 139

https://doi.org/10.1145/2931002.2947705

Published: 22 July 2016 Publication History

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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.

Reference

[1]

Nakamura, T., Maejima, A., and Morishima, S. 2014. Driver Drowsiness Estimation from Facial Expression Features Computer Vision Feature Investigation Using a CG Model. In Proc. VISAPP, vol. 2, 207--214.

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Index Terms

Perception of drowsiness based on correlation with facial image features
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Perception
  2. Machine learning
    1. Machine learning approaches
      1. Learning linear models
        Perceptron algorithm

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Information & Contributors

Information

Published In

SAP '16: Proceedings of the ACM Symposium on Applied Perception

July 2016

149 pages

ISBN:9781450343831

DOI:10.1145/2931002

Conference Chairs:
Eakta Jain
University of Florida
,
Sophie Joerg
Clemson University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2016

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SAP '16

Sponsor:

SIGGRAPH

SAP '16: ACM Symposium on Applied Perception 2016

July 22 - 23, 2016

California, Anaheim

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Overall Acceptance Rate 43 of 94 submissions, 46%

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