L_1-constrained 3-D face sparse reconstruction
Chi Fang · Journal of Tsinghua University(Science and Technology) · 2012
The finesses and robustness of 3-D sparse reconstruction is improved by a modified prior-distribution description of the model parameters in an L1-constrained least squares(L1-LS) reconstruction method,which is a maximum a posteriori(MAP) parameter estimator.The algorithm reconstruction error was evaluated using a BFM 3-D face database.This method gave smaller reconstruction errors than the ordinary least squares(OLS) and L2-constrained least squares(L2-LS) methods and the dynamic component deformable model(DCDM) algorithm.In addition,the reconstruction performance of L1-LS is more robust.Experimental results demonstrate this method more reliably overcomes the severe multicollinearity problem existing in representation bases for the 3-D sparse morphable model than traditional sparse reconstruction methods like OLS or L2-LS to give better reconstruction results.