Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Facial Expression Recognition using Face Alignment and AdaBoost

얼굴정렬과 AdaBoost를 이용한 얼굴 표정 인식

  • Jeong, Kyungjoong (School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology) ;
  • Choi, Jaesik (School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology) ;
  • Jang, Gil-Jin (School of Electronics Engineering, Kyungpook National University)
  • 정경중 (울산과학기술대학교 전기전자컴퓨터공학부) ;
  • 최재식 (울산과학기술대학교 전기전자컴퓨터공학부) ;
  • 장길진 (경북대학교 전자공학부)
  • Received : 2014.08.22
  • Accepted : 2014.10.30
  • Published : 2014.11.25
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Abstract

This paper suggests a facial expression recognition system using face detection, face alignment, facial unit extraction, and training and testing algorithms based on AdaBoost classifiers. First, we find face region by a face detector. From the results, face alignment algorithm extracts feature points. The facial units are from a subset of action units generated by combining the obtained feature points. The facial units are generally more effective for smaller-sized databases, and are able to represent the facial expressions more efficiently and reduce the computation time, and hence can be applied to real-time scenarios. Experimental results in real scenarios showed that the proposed system has an excellent performance over 90% recognition rates.

본 논문에서는 얼굴영상에 나타난 사람의 표정을 인식하기 위해 얼굴검출, 얼굴정렬, 얼굴단위 추출, 그리고 AdaBoost를 이용한 학습 방법과 효과적인 인식방법을 제안한다. 입력영상에서 얼굴 영역을 찾기 위해서 얼굴검출을 수행하고, 검출된 얼굴영상에 대하여 학습된 얼굴모델과 정렬(Face Alignment)을 수행한 후, 얼굴의 표정을 나타내는 단위요소(Facial Units)들을 추출한다. 본 논문에서 제안하는 얼굴 단위요소들을 표정을 표현하기 위한 기본적인 액션유닛(AU, Action Units)의 하위집합으로 눈썹, 눈, 코, 입 부분으로 나눠지며, 이러한 액션유닛에 대하여 AdaBoost 학습을 수행하여 표정을 인식한다. 얼굴유닛은 얼굴표정을 더욱 효율적으로 표현할 수 있고 학습 및 테스트에서 동작하는 시간을 줄여주기 때문에 실시간 응용분야에 적용하기 적합하다. 실험결과, 제안하는 표정인식 시스템은 실시간 환경에서 90% 이상의 우수한 성능을 보여준다.

Keywords

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