Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20339
Title: Depth estimation of face images based on the constrained ICA model
Authors: Sun, ZL
Lam, KM 
Keywords: 3-D face reconstruction
CANDIDE model
constrained independent component analysis (cICA)
overcomplete independent component analysis (ICA)
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information forensics and security, 2011, v. 6, no. 2, 5719166, p. 360-370 How to cite?
Journal: IEEE transactions on information forensics and security 
Abstract: 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.
URI: http://hdl.handle.net/10397/20339
ISSN: 1556-6013
EISSN: 1556-6021
DOI: 10.1109/TIFS.2011.2118207
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