ROBUST DEPTH ESTIMATION FOR EFFICIENT 3D FACE RECONSTRUCTION

This paper proposes a learning based framework for efficient 3D face reconstruction. We transfer the 3D reconstruction into a statistical learning problem of finding appropriate mapping between texture and depth subspaces. Instead of using grayscales to directly estimate the depth, we use local binary pattern (LBP) to further encode the face texture, providing robustness for depth estimation under different illumination conditions. Then the high dimension learning problem between face subspaces is tackled by the kernel partial least squares (PLS) regression. The experimental results show that the proposed method can reconstruct 3D face from single frontal image efficiently and robustly.