Depth estimation of face images based on the constrained ICA model

Authors:
Zhan-Li Sun;Kin-Man Lam
Affiliations:
Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University and Hefei Institute of Intelligent Machines, Chinese Academy of Sciences;Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University
Venue:
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Year:
2010

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Abstract

In this paper, we propose a novel and efficient algorithm to reconstruct the 3D structure of a human face from one or a number of its 2D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a non-frontal-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. The CANDIDE model is also employed to design a reference signal in our algorithm. Moreover, a model-integration method is proposed to improve the depth-estimation accuracy when multiple non-frontal-view face images are available. Experimental results on a real 3D face image database demonstrate the feasibility and efficiency of the proposed method.