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浙江大学学报(工学版)
浙江大学学报(工学版)
计算机技术、电子通信技术     
基于关键点和局部特征的三维人脸识别
郭梦丽, 达飞鹏, 邓星, 盖绍彦
东南大学 自动化学院,江苏 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,江苏 南京 210096
3D face recognition based on keypoints and local feature
GUO Meng-li, DA Fei-peng, DENG Xing, GAI Shao-yan
School of Automation, Southeast University, Nanjing 210096, China; 
Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing 210096, China
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摘要：

为了减少表情变化对三维人脸识别的影响,提出一种基于关键点和局部特征的三维人脸识别算法.根据有价值轮廓线和平均曲率检测关键点;根据DAISY描述符的形式构造局部特征的空间结构，采用形状指数直方图、倾斜角直方图和方向角直方图作为局部特征,进行关键点匹配;并利用匹配成功的关键点数目衡量2个人脸曲面的相似度.基于FRGC v2.0数据库和Bosphorus数据库开展身份识别实验,获得了96.9%和95.8%的Rank-1识别率.实验证明所提出的方法识别性能较好,并对表情变化识别具有一定的鲁棒性.
Abstract:
A novel 3D face recognition algorithm based on keypoints and local feature was proposed to reduce the impact of expression variations in 3D face recognition. First, keypoints were detected according to the valuable profile and mean curvature. Then, the spatial structure of the local feature was set as DAISY descriptor. The local feature was obtained by concatenating the histograms of shape indices, slant angles and direction angles. Finally, the match of keypoints was performed; the number of keypoints which were matched properly was used to measure the similarity of two face surfaces. The identification experiments were carried out on the FRGC v2.0 database and the Bosphorus database. As a result, the rank-one recognition rates of the two experiments are 96.9% and 95.8%, respectively. The experimental results demonstrate that the proposed algorithm can achieve higher recognition performance and is robust to expression variations.
出版日期: 2017-03-01
CLC:  TP 391  
基金资助:

国家自然科学基金资助项目（61405034,51475092,51175081);高等学校博士学科点专项科研基金资助项目（20130092110027）;中央高校基本科研业务费专项资金资助项目;江苏省普通高校研究生科研创新计划资助项目（KYLX15_0117）
通讯作者: 达飞鹏, 男, 教授. ORCID: 0000-0001-5475-3145.     E-mail: dafp@seu.edu.cn
作者简介: 郭梦丽(1990—), 女, 硕士,从事计算机视觉与三维人脸识别研究.ORCID: 0000-0001-7685-7527. E-mail: mengligml@163.com
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郭梦丽, 达飞鹏, 邓星, 盖绍彦. 基于关键点和局部特征的三维人脸识别[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.03.021.

GUO Meng-li, DA Fei-peng, DENG Xing, GAI Shao-yan. 3D face recognition based on keypoints and local feature. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2017.03.021.

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