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Emotion Training: Image Color Transfer with Facial Expression and Emotion Recognition

감정 트레이닝: 얼굴 표정과 감정 인식 분석을 이용한 이미지 색상 변환

  • Received : 2018.03.04
  • Accepted : 2018.08.15
  • Published : 2018.09.01

Abstract

We propose an emotional training framework that can determine the initial symptom of schizophrenia by using emotional analysis method through facial expression change. We use Emotion API in Microsoft to obtain facial expressions and emotion values at the present time. We analyzed these values and recognized subtle facial expressions that change with time. The emotion states were classified according to the peak analysis-based variance method in order to measure the emotions appearing in facial expressions according to time. The proposed method analyzes the lack of emotional recognition and expressive ability by using characteristics that are different from the emotional state changes classified according to the six basic emotions proposed by Ekman. As a result, the analyzed values are integrated into the image color transfer framework so that users can easily recognize and train their own emotional changes.

본 논문은 얼굴의 표정 변화를 통해 감정을 분석하는 방법으로 조현병의 초기 증상을 스스로 인지할 수 있는 감정 트레이닝 프레임워크를 제안한다. 먼저, Microsoft의 Emotion API를 이용하여 캡처된 얼굴 표정의 사진으로부터 감정값을 얻고, 피크 분석 기반 표준편차로 시간에 따라 변화하는 얼굴 표정의 미묘한 차이를 인식해 감정 상태를 각각 분류한다. 그리하여 Ekman이 제안한 여섯 가지 기본 감정 상태에 반하는 감정들의 정서 및 표현능력이 결핍된 부분에 대해 분석하고, 그 값을 이미지 색상 변환 프레임워크에 통합시켜 사용자 스스로 감정의 변화를 쉽게 인지하고 트레이닝 할 수 있도록 하는 것이 최종목적이다.

Keywords

References

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