[1]吕士文,达飞鹏,邓星.基于区域改进LBP的三维人脸识别[J].东南大学学报(自然科学版),2015,45(4):678-682.[doi:10.3969/j.issn.1001-0505.2015.04.012]
 Lü Shiwen,Da Feipeng,Deng Xing.3D face recognition method based on regional enhanced local binary pattern[J].Journal of Southeast University (Natural Science Edition),2015,45(4):678-682.[doi:10.3969/j.issn.1001-0505.2015.04.012]
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基于区域改进LBP的三维人脸识别()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
45
期数:
2015年第4期
页码:
678-682
栏目:
计算机科学与工程
出版日期:
2015-07-20

文章信息/Info

Title:
3D face recognition method based on regional enhanced local binary pattern
作者:
吕士文达飞鹏邓星
东南大学自动化学院, 南京210096; 东南大学复杂工程系统测量与控制教育部重点实验室, 南京210096
Author(s):
Lü Shiwen Da Feipeng Deng Xing
School of Automation, Southeast University, Nanjing 210096, China
Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
关键词:
三维人脸识别 改进LBP 深度图 稀疏表示分类器
Keywords:
3D face recognition enhanced local binary pattern depth image sparse representation classifier
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2015.04.012
摘要:
针对三维人脸识别中的表情问题,提出一种基于区域改进局部二值模式(LBP)的三维人脸识别算法. 首先将预处理后的三维点云转化为深度图并进行归一化处理;然后根据表情对人脸的影响,利用二元掩膜提取人脸的刚性、半刚性和非刚性区域;对每个局部区域,计算其改进LBP特征,并用等价模式进行表征;最后使用稀疏表示分类器(SRC)对单个局部区域进行识别实验,并使用带权重的稀疏表示分类器(W-SRC)对刚性和半刚性区域进行决策级融合,给出最终识别结果.在FRGC v2.0人脸数据库上的实验结果表明,该方法具有较好的鲁棒性和较高的识别精度.
Abstract:
Aiming at the problem of facial expression, a 3D face recognition method based on regional enhanced local binary pattern(LBP)is proposed. First, the depth image converted from the preprocessed 3D point clouds is normalized. Then, rigid region, semi-rigid region and non-rigid region are extracted by binary masks according to the influence of different expressions on faces. The feature represented by the uniform pattern of enhanced LBP(eLBP)is calculated in every local region. Finally, for single region, classification experiments with sparse representation classifier(SRC)are conducted. Score-level fusion with weighted SRC(W-SRC)for rigid region and semi-rigid region is also tested and compared. The experiments on FRGC v2.0 database demonstrate that the proposed method is robust and efficient.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-01-21.
作者简介: 吕士文(1990—),男,硕士生;达飞鹏(联系人),男,博士,教授,博士生导师,dafp@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51175081,51475092,61405034)、教育部博士点基金资助项目(20130092110027).
引用本文: 吕士文,达飞鹏,邓星.基于区域改进LBP的三维人脸识别[J].东南大学学报:自然科学版,2015,45(4):678-682. [doi:10.3969/j.issn.1001-0505.2015.04.012]
更新日期/Last Update: 2015-07-20