3D face recognition based on multiple keypoint descriptors and sparse representation.
3D face recognition based on multiple keypoint descriptors and sparse representation.
复制标题
基于多关键点描述符和稀疏表示的3D人脸识别
DOI：
10.1371/journal.pone.0100120
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lu J
中科院分区：
文献类型：
--
作者：
Zhang L;Ding Z;Li H;Shen Y;Lu J
近年来，人们对开发3D人脸识别方法的兴趣越来越大。然而，3D扫描通常会遇到缺失部分，大面部表情和遮挡的问题。为了在现实世界的应用中发挥作用，3D人脸识别方法应该能够应对这些挑战。在本文中，我们提出了一种新的通用方法来处理三维人脸识别问题，利用多个关键点描述符（MKD）和稀疏表示为基础的分类（SRC）。我们简称所提出的方法为3DMKDSRC。具体来说，使用3DMKDSRC，每个3D面部扫描被表示为通过meshSIFT从关键点提取的一组描述符向量。图库样本的描述符向量形成图库字典。给定一个探头的三维人脸扫描，它的描述符提取，然后可以确定其身份，通过使用多任务SRC。所提出的3DMKDSRC方法不需要在两次人脸扫描之间进行预对准，并且对丢失数据、遮挡和表情的问题具有相当强的鲁棒性。它优于其他领先的3D人脸识别方案已经通过在三个基准数据库Bosphorus，GavabDB和FRGC2.0上进行的广泛实验得到了证实。3DMKDSRC的Matlab源代码和相关评估结果可在http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm上公开获得。
Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm.
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DOI：
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发表时间：
2004-11-01
影响因子：
19.5
作者：
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影响因子：
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发表时间：
2007-12-01
影响因子：
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作者：
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影响因子：
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