Lock3DFace:一个大规模的低成本Kinect 3D人脸数据库

Jinjin Zhang, Di Huang, Yunhong Wang, Jia Sun
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引用次数: 47

摘要

在本文中,我们提出了一个由低成本Kinect 3D人脸视频组成的大规模数据库,即Lock3DFace,用于3D人脸分析,特别是3D人脸识别(FR)。据我们所知,Lock3DFace是目前公共学术使用的最大的低成本3D人脸数据库。3D样本具有高噪声,包含表情、姿态、遮挡、时间推移等多种变化,其对应的纹理和近红外通道在光照条件和辐射强度上都有变化,从而可以在复杂情况下评估FR方法。此外,基于Lock3DFace,我们设计了低成本3D人脸识别的标准实验方案,并给出了属于不同场景的单个子集的基准性能,以便将来进行公平比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lock3DFace: A large-scale database of low-cost Kinect 3D faces
In this paper, we present a large-scale database consisting of low cost Kinect 3D face videos, namely Lock3DFace, for 3D face analysis, particularly for 3D Face Recognition (FR). To the best of our knowledge, Lock3DFace is currently the largest low cost 3D face database for public academic use. The 3D samples are highly noisy and contain a diversity of variations in expression, pose, occlusion, time lapse, and their corresponding texture and near infrared channels have changes in lighting condition and radiation intensity, allowing for evaluating FR methods in complex situations. Furthermore, based on Lock3DFace, we design the standard experimental protocol for low-cost 3D FR, and give the baseline performance of individual subsets belonging to different scenarios for fair comparison in the future.
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