Depth estimation of face images based on the constrained ICA model

Zhan Li Sun, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

46 Citations (Scopus)


In this paper, we propose a novel and efficient algorithm to reconstruct the 3-D structure of a human face from one or a number of its 2-D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a nonfrontal-view face image is at first formulated as a constrained independent component analysis (cICA) model. Then, the overcomplete ICA problem is converted into a normal ICA problem by incorporating a prior from the CANDIDE 3-D face model. Furthermore, the CANDIDE model is employed to construct a reference signal that is used in both the initialization and the objective function of the cICA model. Moreover, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. An important advantage of the proposed algorithm is that no frontal-view face image is required for the estimation of the corresponding 3-D face structure. Experimental results on a real 3-D face image database demonstrate the feasibility and efficiency of the proposed method.
Original languageEnglish
Article number5719166
Pages (from-to)360-370
Number of pages11
JournalIEEE Transactions on Information Forensics and Security
Issue number2
Publication statusPublished - 1 Jun 2011


  • 3-D face reconstruction
  • CANDIDE model
  • constrained independent component analysis (cICA)
  • overcomplete independent component analysis (ICA)

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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