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In this paper, we propose a novel algorithm for reconstructing the 3D shape and texture of human faces from two stereo images, which are captured from calibrated cameras. Our approach works in a sparse to dense manner: we first build a coarse shape estimation based on 3D keypoints, and then use a linear morphable model to efficiently match the detail shape and texture. Compared with the previous works,...
We propose a method to generate a highly accurate 3D face model from a set of wide-baseline images in a weakly calibrated setup. Our approach is purely data driven, and produces faithful 3D models without any pre-defined models, unlike other statistical model-based approaches. Our results do not rely upon a critical initialization step nor parameters for optimization steps. We process 5 images (including...
Given information from many cameras, one can hope to get a complete 3D representation of an object. Pintavirooj and Sangworasil exploit this idea and present a system that records sequentially images from multiple view points to reconstruct a 3D shape of a static object of interest [1]. For instance, using a 60 angle of view on the image, they manage to get its accurate 3D reconstruction [1]. Unfortunately,...
We describe a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences...
In this article we explore the use of methodologies for 3D reconstruction from multiple images to recognize faces. We try to devise a strategy to tackle the problem of recognizing faces from images exhibiting strong pose (rotation and occlusion) and without prior knowledge (uncalibrated cameras, images from different sources). We do so by framing the recognition in the context of 3D structure from...
A framework for automatic human head pose estimation from single view images is proposed. The 6DOF head pose was estimated using pose from orthography and scaling with iterations (POSIT) where a statistical anthropometric 3D rigid model is used as an approximation of the human head, combined with active appearance models (AAM) for facial features extraction and tracking. The overall performance of...
This paper presents an efficient algorithm of face alignment of multiple images by 2.5D Active Appearance Model (AAM). Currently with wide availability of inexpensive webcams a multi-view system is as practical as mono-view. To manage these multiple information obtained from multiview system we propose a new optimization technique of AAM. Our technique is based on Pareto multi-objective genetic optimization...
We present a practical algorithm that provably achieves the global optimum for a class of bilinear programs commonly arising in computer vision applications. Our approach relies on constructing tight convex relaxations of the objective function and minimizing it in a branch and bound framework. A key contribution of the paper is a novel, provably convergent branching strategy that allows us to solve...
3D acquisition techniques to measure dynamic scenes and deformable objects with little texture are extensively researched for applications like the motion capturing of human facial expression. To allow such measurement, several techniques using structured light have been proposed. These techniques can be largely categorized into two types. The first involves techniques to temporally encode positional...
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