Korean Journal of Computational Design and Engineering (한국CDE학회논문집)

Society for Computational Design and Engineering (한국CDE학회)
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3D Face Alignment and Normalization Based on Feature Detection Using Active Shape Models : Quantitative Analysis on Aligning Process

ASMs을 이용한 특징점 추출에 기반한 3D 얼굴데이터의 정렬 및 정규화 : 정렬 과정에 대한 정량적 분석

  • 신동원 (금오공과대학교 기계공학부) ;
  • 박상준 (금오공과대학교 자동차공학과) ;
  • 고재필 (금오공과대학교 컴퓨터공학과)
  • Published : 2008.12.31
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Abstract

The alignment of facial images is crucial for 2D face recognition. This is the same to facial meshes for 3D face recognition. Most of the 3D face recognition methods refer to 3D alignment but do not describe their approaches in details. In this paper, we focus on describing an automatic 3D alignment in viewpoint of quantitative analysis. This paper presents a framework of 3D face alignment and normalization based on feature points obtained by Active Shape Models (ASMs). The positions of eyes and mouth can give possibility of aligning the 3D face exactly in three-dimension space. The rotational transform on each axis is defined with respect to the reference position. In aligning process, the rotational transform converts an input 3D faces with large pose variations to the reference frontal view. The part of face is flopped from the aligned face using the sphere region centered at the nose tip of 3D face. The cropped face is shifted and brought into the frame with specified size for normalizing. Subsequently, the interpolation is carried to the face for sampling at equal interval and filling holes. The color interpolation is also carried at the same interval. The outputs are normalized 2D and 3D face which can be used for face recognition. Finally, we carry two sets of experiments to measure aligning errors and evaluate the performance of suggested process.

Keywords

References

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