基于Sparse ICP 的三维点云耳廓识别

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。

3D Ear Point Clouds Recognition Using Sparse ICP

A novel 3D ear recognition method is proposed in this paper. Firstly, using the PCA andSVD algorithm to normalize 3D ear point clouds model, and adjust the position and posture of all earpoint cloud models in the database. Then, based on the Iannarelli system, we extract four local featureregions of 3D ear model, and match them with Sparse ICP algorithm. Finally we match 3D earmodels according to the distance between their corresponding points. The experiments show that ouralgorithm has higher recognition accuracy and efficiency compared with other algorithms.