Recovering the 3D shape and poses of face images based on the similarity transform

Hei Sheung Koo, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


In this paper, a new algorithm is proposed to derive the 3D structure of a human face from a group of face images under different poses. Based on the corresponding 2D feature points of the respective images, their respective poses and the depths of the feature points can be estimated based on measurements using the similarity transform. To accurately estimate the pose of and the 3D information about a human face, the genetic algorithm (GA) is applied. Our algorithm does not require any prior knowledge of camera calibration, and has no limitation on the possible poses or the scale of the face images. It also provides a means to evaluate the accuracy of the constructed 3D face model based on the similarity transform of the 2D feature point sets. Our approach can also be extended to face recognition to alleviate the effect of pose variations. Experimental results show that our proposed algorithm can construct a 3D face structure reliably and efficiently.
Original languageEnglish
Pages (from-to)712-723
Number of pages12
JournalPattern Recognition Letters
Issue number6
Publication statusPublished - 15 Apr 2008


  • 3D reconstruction
  • Face recognition
  • Genetic algorithm
  • Similarity transform

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering


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