IEEE Trans Image Process - Depth estimation of face images using the nonlinear least-squares model.

Tópicos

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Resumo

In this paper, we propose an efficient algorithm to reconstruct the 3D structure of a human face from one or more of its 2D images with different poses. In our algorithm, the nonlinear least-squares model is first employed to estimate the depth values of facial feature points and the pose of the 2D face image concerned by means of the similarity transform. Furthermore, different optimization schemes are presented with regard to the accuracy levels and the training time required. Our algorithm also embeds the symmetrical property of the human face into the optimization procedure, in order to alleviate the sensitivities arising from changes in pose. In addition, the regularization term, based on linear correlation, is added in the objective function to improve the estimation accuracy of the 3D structure. Further, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. Experimental results on the 2D and 3D databases demonstrate the feasibility and efficiency of the proposed methods.

Resumo Limpo

paper propos effici algorithm reconstruct d structur human face one d imag differ pose algorithm nonlinear leastsquar model first employ estim depth valu facial featur point pose d face imag concern mean similar transform furthermor differ optim scheme present regard accuraci level train time requir algorithm also emb symmetr properti human face optim procedur order allevi sensit aris chang pose addit regular term base linear correl ad object function improv estim accuraci d structur modelintegr method propos improv depthestim accuraci multipl nonfrontalview face imag avail experiment result d d databas demonstr feasibl effici propos method

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