한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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Multiple Active Appearance Model을 이용한 얼굴 특징 추출 기법

Facial Feature Extraction using Multiple Active Appearance Model

  • 박현준 (부산대학교 컴퓨터공학과학과) ;
  • 김광백 (한국멀티미디어학회 국제운영부) ;
  • 차의영 (부산대학교 컴퓨터공학과)
  • 투고 : 2013.06.11
  • 심사 : 2013.08.23
  • 발행 : 2013.08.30
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초록

영상에서 얼굴 및 얼굴 특징을 추출하기 위한 기법으로 active appearance model(AAM)이 있다. 본 논문에서는 두 개의 AAM을 이용하여 얼굴 특징을 추출하는 multiple active appearance model(MAAM) 기법을 제안한다. 두 개의 AAM은 학습 데이터에 대한 파라미터를 조절하여 상반되는 장단점을 가지도록 생성하고, 서로의 단점을 보완할 수 있도록 한다. 제안된 방법의 성능을 평가하기 위해 100장의 영상에 대해서 얼굴 특징추출 실험을 하였다. 실험 결과 기존의 AAM 하나만을 사용하는 기법에 비해 적은 횟수의 피팅만으로도 정확도 높은 결과를 얻을 수 있었다.

Active Appearance Model(AAM) is one of the facial feature extraction techniques. In this paper, we propose the Multiple Active Appearance Model(MAAM). Proposed method uses two AAMs. Each AAM trains using different training parameters. It causes that each AAM has different strong points. One AAM complements the weak points in the other AAM. We performed the facial feature extraction on the 100 images to verify the performance of MAAM. Experiment results show that MAAM gives more accurate results than AAM with less fitting iteration.

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참고문헌

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피인용 문헌

  1. A Study on Facial Wrinkle Detection using Active Appearance Models vol.12, pp.7, 2014, https://doi.org/10.14400/JDC.2014.12.7.239