摘要
提出一种新颖的三维耳廓识别方法,首先基于PCA和SVD分解对三维耳廓点云模型进行归一化预处理,以统一数据库中所有耳廓点云模型的位置与姿态;然后基于Iannarelli分类系统提取三维耳廓的4个局部特征区域,并利用Sparse ICP算法对局部特征区域进行匹配;最后根据局部特征区域中对应点间的距离判断耳廓之间的差异测度,实现耳廓形状识别。实验证明,本文算法与其他算法相比具有较高的识别精度和识别效率。
A novel 3D ear recognition method is proposed in this paper. Firstly, using the PCA and SVD algorithm to normalize 3D ear point clouds model, and adjust the position and posture of all ear point cloud models in the database. Then, based on the Iannarelli system, we extract four local feature regions of 3D ear model, and match them with Sparse ICP algorithm. Finally we match 3D ear models according to the distance between their corresponding points. The experiments show that our algorithm has higher recognition accuracy and efficiency compared with other algorithms.
出处
《图学学报》
CSCD
北大核心
2015年第6期862-867,共6页
Journal of Graphics
基金
国家自然科学基金资助项目(60873110
61170143
61472170)
智能通信软件与多媒体北京市重点实验室开发课题(ITSM201301)
