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Depth Estimation of Face Images Based on the Constrained ICA Model

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2 Author(s)
Zhanli Sun ; Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hong Kong, China ; Kin-Man Lam

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.

Published in:

Information Forensics and Security, IEEE Transactions on  (Volume:6 ,  Issue: 2 )
Biometrics Compendium, IEEE