3D face recognition based on high-resolution 3D face modeling from frontal and profile views

L. Yin, Matt T. Yourst
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引用次数: 28

Abstract

This paper presents a novel face recognition system which considers information from both frontal and profile view images and videos. In the system, we recover facial texture details by increasing the input image resolution, construct an accurate 3D face model from two views of a face, and explore both 3D shape and texture informations for an optimal match and identification based on a 3D face model database. Unlike many existing 3D face recognition systems where the 3D model is taken as a bridge for synthesizing textures of various poses from the viewing sphere, we explicitly use 3D geometric information to index the reference database in order to increase the matching accuracy. This work is the first step toward the development of a face recognition solution by exploring 3D context explicitly. The system consists of three major modules, including (1) 3D face model database creation based on two views' face images input; (2) query face model synthesis from two views' face video input; (3) matching between the database model and the query model using a hybrid method (i.e., shape and texture). The high resolution face model reconstruction is critical for success of the system. Five key components are developed: (1) Facial silhouette extraction; (2) facial texture detail reconstruction based on a novel algorithm: Hyper-resolution image enhancement; (3) feature detection from two views of a face; (4) face model instantiation by adapting the model to the resolution-increased input image; (5) 3D facial geometric information reconstruction using two views' models. The system has been tested using 60 subjects and has shown the correct match rate at 91.2%.
基于正面和侧面视图的高分辨率3D人脸建模的3D人脸识别
提出了一种综合考虑正面、侧面图像和视频信息的人脸识别系统。在该系统中,我们通过提高输入图像分辨率来恢复面部纹理细节,从人脸的两个视图构建精确的三维人脸模型,并在三维人脸模型数据库的基础上探索三维形状和纹理信息,以实现最优匹配和识别。与现有的许多3D人脸识别系统以三维模型为桥梁,从观察球中合成各种姿态纹理不同,我们明确地使用三维几何信息来索引参考数据库,以提高匹配精度。这项工作是通过明确探索3D环境来开发人脸识别解决方案的第一步。该系统包括三个主要模块:(1)基于两视图人脸图像输入的三维人脸模型数据库创建;(2)从两视图的人脸视频输入查询人脸模型合成;(3)采用混合方法(即形状和纹理)对数据库模型和查询模型进行匹配。高分辨率的人脸模型重建是系统成功的关键。开发了五个关键部分:(1)面部轮廓提取;(2)基于超分辨率图像增强新算法的面部纹理细节重建;(3)从人脸的两个视图进行特征检测;(4)人脸模型实例化,使模型适应提高分辨率的输入图像;(5)利用两视图模型重建三维人脸几何信息。该系统已经对60个对象进行了测试,结果显示正确的匹配率为91.2%。
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