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Emotion Classification Method Using Various Ocular Features

다양한 눈의 특징 분석을 통한 감성 분류 방법

  • 김윤경 (상명대학교 대학원 컴퓨터과학과) ;
  • 원명주 (상명대학교 대학원 감성공학과) ;
  • 이의철 (상명대학교 컴퓨터과학과)
  • Received : 2014.06.25
  • Accepted : 2014.10.14
  • Published : 2014.10.28

Abstract

In this paper, emotion classification was performed by using four ocular features extracted from near-infrared camera image. According to comparing with previous work, the proposed method used more ocular features and each feature was validated as significant one in terms of emotion classification. To minimize side effects on ocular features caused by using visual stimuli, auditory stimuli for causing two opposite emotion pairs such as "positive-negative" and "arousal-relaxation" were used. As four features for emotion classification, pupil size, pupil accommodation rate, blink frequency, and eye cloased duration were adopted which could be automatically extracted by using lab-made image processing software. At result, pupil accommodation rate and blink frequency were statistically significant features for classification arousal-relaxation. Also, eye closed duration was the most significant feature for classification positive-negative.

Keywords

Emotion Classification;Pupil Size;Pupil Accommodation Rate;Blink Frequency;Eye Closed Duration

Acknowledgement

Supported by : 상명대학교

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