Human ear recognition in 3D
Human ear recognition in 3D
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DOI：
10.1109/tpami.2007.1005
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发表时间：
2007-04-01
影响因子：
23.6
通讯作者：
Bhanu, Bir
中科院分区：
文献类型：
--
作者：
Chen, Hui;Bhanu, Bir
人耳是一类新的相对稳定的生物识别技术，最近引起了研究人员的注意。在本文中，我们提出了使用3D Ear Biometrics的完整人类识别系统。该系统由3D耳朵检测，3D耳朵识别和3D耳朵验证组成。为了进行耳朵检测，我们提出了一种新方法，该方法使用单个参考3D耳朵形型号，并将耳螺旋和抗固定零件定位在注册的2D颜色和3D范围图像中。对于使用范围图像的耳朵识别和验证，提出了两个新的表示。其中包括从检测算法和在特征点计算的局部表面贴片（LSP）表示获得的EAR螺旋/抗固定表示。局部表面描述符的特征是质心，局部表面类型和2D直方图。 2D直方图显示了形状索引值的发生频率与参考特征点的正常角度与其邻居的角度。两种形状表示都用于估计画廊探针对之间的初始刚性变换。这种转换应用于画廊集合中的耳朵的选定位置，并且修改了迭代的最接近点（ICP）算法用于迭代地改进转换，从根本平均值的意义上讲，将画廊的耳朵和探测到最佳对齐方式正方形错误。在姿势变化下具有902张图像的155名受试者的UCR数据集以及Notre University of Notre Dame数据集，其中302位具有延时库探针对的受试者，以比较和证明拟议算法的有效性和系统。
Human ear is a new class of relatively stable biometrics that has drawn researchers' attention recently. In this paper, we propose a complete human recognition system using 3D ear biometrics. The system consists of 3D ear detection, 3D ear identification, and 3D ear verification. For ear detection, we propose a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix parts in registered 2D color and 3D range images. For ear identification and verification using range images, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation computed at feature points. A local surface descriptor is characterized by a centroid, a local surface type, and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values versus the angles between the normal of reference feature point and that of its neighbors. Both shape representations are used to estimate the initial rigid transformation between a gallery-probe pair. This transformation is applied to selected locations of ears in the gallery set and a modified Iterative Closest Point (ICP) algorithm is used to iteratively refine the transformation to bring the gallery ear and probe ear into the best alignment in the sense of the least root mean square error. The experimental results on the UCR data set of 155 subjects with 902 images under pose variations and the University of Notre Dame data set of 302 subjects with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of the proposed algorithms and the system.