| Computer Technology, Electronic Communications Technologies |
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| 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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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.
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Published: 01 March 2017
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Cite this article:
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), 2017, 51(3): 584-589.
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基于关键点和局部特征的三维人脸识别
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.
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