Yuhang Wu

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In this paper, we propose a robust method for monocular face shape reconstruction (MFSR) using a sparse set of facial landmarks that are detected by most of the off-the-shelf landmark detectors. Different from the classical shape-from-shading framework, we formulate the MFSR problem as a TwoFold Coupled Structure Learning (2FCSL) process, which consists of(More)
The problem of estimating the 3D shape of human faces from single images is of great interest and has attracted considerable research effort. Many approaches recently proposed to solve this problem could be considered extensions of Shape-from-Shading (SFS) methods, where a 3D shape is optimized to generate 2D renderings that match the input images [1, 5,(More)
—3D-Model-Aided 2D face recognition (MaFR) has attracted a lot of attention in recent years. By registering a 3D model, facial textures of the gallery and the probe can be lifted and aligned in a common space, thus alleviating the challenge of pose variations. One obstacle preventing accurate registration is the 3D-2D pose estimation, which is easily(More)
Despite the great progress achieved in unconstrained face recognition, pose variations still remain a challenging and unsolved practical issue. We propose a novel framework for multi-view face recognition based on extracting and matching pose-robust face signatures from 2D images. Specifically, we propose an efficient method for monocu-lar 3D face(More)
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