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Frontal Face Video Analysis for Detecting Fatigue States

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(6), pp.43-52
  • DOI : 10.9708/jksci.2022.27.06.043
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 20, 2022
  • Accepted : June 13, 2022
  • Published : June 30, 2022

Simyeong Cha 1 Jongwoo Ha 1 Soungwoong Yoon 1 Chang-Won Ahn 1

1바이브컴퍼니

Accredited

ABSTRACT

We can sense somebody's feeling fatigue, which means that fatigue can be detected through sensing human biometric signals. Numerous researches for assessing fatigue are mostly focused on diagnosing the edge of disease-level fatigue. In this study, we adapt quantitative analysis approaches for estimating qualitative data, and propose video analysis models for measuring fatigue state. Proposed three deep-learning based classification models selectively include stages of video analysis: object detection, feature extraction and time-series frame analysis algorithms to evaluate each stage's effect toward dividing the state of fatigue. Using frontal face videos collected from various fatigue situations, our CNN model shows 0.67 accuracy, which means that we empirically show the video analysis models can meaningfully detect fatigue state. Also we suggest the way of model adaptation when training and validating video data for classifying fatigue.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.