대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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3차원 얼굴 인식을 위한 오류 보상 특이치 분해 기반 얼굴 포즈 추정

Head Pose Estimation Using Error Compensated Singular Value Decomposition for 3D Face Recognition

  • 송환종 (연세대학교 전기전자공학과 생체인식연구센터) ;
  • 양욱일 (연세대학교 전기전자공학과 생체인식연구센터) ;
  • 손광훈 (연세대학교 전기전자공학과 생체인식연구센터)
  • 발행 : 2003.11.01
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⟨ 이전 논문 다음 논문 ⟩

초록

대부분의 얼굴인식 시스템은 현재 2차원 영상을 기반으로 많은 분야에 응용되고 있다. 그러나 2차원 얼굴인식 시스템은 심하게 변화된 얼굴 포즈에 강인한 얼굴인식이 매우 어렵다. 이에 얼굴 포즈 추정은 정면 영상이 아닐 경우 인식률 향상을 위한 필수적인 과정이라 할 수 있다. 그러므로, 본 논문은 3차원 얼굴인식을 위한 새로운 얼굴 포즈 추정 방식을 제안한다 먼저 3차원 거리(range) 영상이 입력될 때 얼굴 곡선에 기반한 자동 얼굴 특징점 추출 기법을 적용한다. 추출된 특징점을 바탕으로 오류 보상 특이치 분해를 적용 한 새로운 3차원 얼굴 포즈 추정 방식을 제안한다. 특이치 분해를 이용하여 초기 회전각을 획득한 후 존재하는 오류를 보다 세밀하게 보상한다. 제안 알고리즘은 정규화된 3차원 얼굴 공간에서 추출된 특징점의 기하학적 위치를 이용하여 수행된다. 또한 3차원 얼굴인식을 위하여 3차원 최근접 이웃 분류기를 이용한 데이터베이스내에서 후보 얼굴을 선택하는 방식을 제안한다. 실험 결과를 통해 다양한 얼굴 포즈에 대하여 제안 알고리즘의 효율성과 타당성을 검증하였다.

Most face recognition systems are based on 2D images and applied in many applications. However, it is difficult to recognize a face when the pose varies severely. Therefore, head pose estimation is an inevitable procedure to improve recognition rate when a face is not frontal. In this paper, we propose a novel head pose estimation algorithm for 3D face recognition. Given the 3D range image of an unknown face as an input, we automatically extract facial feature points based on the face curvature. We propose an Error Compensated Singular Value Decomposition (EC-SVD) method based on the extracted facial feature points. We obtain the initial rotation angle based on the SVD method, and perform a refinement procedure to compensate for remained errors. The proposed algorithm is performed by exploiting the extracted facial features in the normaized 3D face space. In addition, we propose a 3D nearest neighbor classifier in order to select face candidates for 3D face recognition. From simulation results, we proved the efficiency and validity of the proposed algorithm.

키워드

참고문헌

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