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首页> 外文会议>2013 Seventh International Conference on Distributed Smart Cameras >Deformable Generic Elastic Models from a single 2D image for facial expression and large pose face together synthesis and recognition
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Deformable Generic Elastic Models from a single 2D image for facial expression and large pose face together synthesis and recognition

机译：来自单个2D图像的可变形通用弹性模型，用于面部表情和大姿态面部的合成和识别

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摘要

In this paper, we propose an efficient method to reconstruct the 3D models of a human face from a single 2D face image robustness under a variety facial expressions using the Deformable Generic Elastic Model (D-GEM). We extended the Generic Elastic Model (GEM) approach and combined it with statistical information of the human face and deformed generic depth models by computing the distance around face lips. Particularly, we demonstrate that D-GEM can approximate the 3D shape of the input face image more accurately, achieving a better and higher quality of 3D face modeling and reconstruction robustness under a variety of facial expressions compared to the original GEM and Gender and Ethnicity-GEM (GE-GEM) approach. It has been tested on an available 2D face database and new synthesized facial expression and large pose changes together from gallery images. We acquire promising results for handling pose and expression changes based on the proposed method compared to the GEM and GE-GEM.
机译：在本文中，我们提出了一种有效的方法，利用可变形通用弹性模型（D-GEM）从多种表情下的单个2D面部图像鲁棒性重建人脸的3D模型。我们扩展了通用弹性模型（GEM）方法，并将其与人脸的统计信息相结合，并通过计算脸部嘴唇周围的距离来变形了通用深度模型。特别是，我们证明了与原始GEM和“性别和种族”相比，D-GEM可以更准确地近似输入面部图像的3D形状，在各种面部表情下实现更好，更高质量的3D面部建模和重建鲁棒性。 GEM（GE-GEM）方法。它已在可用的2D人脸数据库和新的合成人脸表情以及来自画廊图像的大姿势更改中进行了测试。与GEM和GE-GEM相比，基于所提出的方法，我们在处理姿势和表情变化方面获得了可喜的结果。

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