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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)
Mobile health (mHealth), as an important development direction of eHealth, is an innovative application of spatial information technology used in health field. In this paper, the key technology of location based mobile health system is studied and the system prototype is designed and implemented. The mobile monitoring terminal receives data collected by the(More)
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)
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 monocular 3D face(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)
In spite of recent progress achieved in near-frontal face recognition, the problem of pose variations prevalent in 2D facial images captured in the wild still remains a challenging and unsolved issue. Among existing approaches of pose-invariant face recognition, 3D-aided methods have been demonstrated effective and promising. In this paper, we present an(More)
Head pose estimation helps to align a 3D face model to a 2D image, which is critical to research requiring dense 2D-to-2D or 3D-to-2D correspondence. Traditional pose estimation relies strongly on the accuracy of landmarks, so it is sensitive to missing or incorrect landmarks. In this paper, we propose a landmark-free approach to estimate the pose(More)
In order to find a method to eliminate the GPS receiver’s interference, the paper studied the principle of two conventional adaptive algorithms: Sample Matrix Inversion (SMI) adaptive algorithm and Gram-Schimit (GS) algorithm based on adaptive digital wave beam producing process. This paper analyzed a new algorithm: Improved Gram-Schmit (IGS)(More)
Facial landmark localization is a fundamental module for face recognition. Current common approach for facial landmark detection is cascaded regression, which is composed by two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features in each step and the whole system could be(More)
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