Iris Matching Based on Personalized Weight Map
Iris Matching Based on Personalized Weight Map
复制标题
基于个性化权重图的虹膜匹配
DOI：
10.1109/tpami.2010.227
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发表时间：
2011-09-01
影响因子：
23.6
通讯作者：
Tan, Tieniu
中科院分区：
文献类型：
--
作者：
Dong, Wenbo;Sun, Zhenan;Tan, Tieniu
虹膜识别通常包括三个步骤，即虹膜图像预处理、特征提取和特征匹配。虹膜识别的前两个步骤已经得到了很好的研究，但最后一个步骤的解决较少。每个人的虹膜都有其独特的视觉模式，不同区域的局部图像特征也不同，这导致不同虹膜区域的特征编码在鲁棒性和显著性上存在显著差异。然而，大多数最先进的虹膜识别方法使用统一匹配策略，其中从同一个人的不同区域或不同个体的同一区域提取的特征被认为是同等重要的。本文提出了一种个性化的虹膜匹配策略，该策略使用从同一虹膜类别的训练图像中学习到的特定类别的权重图。将成功识别的虹膜图像作为新的训练数据，在虹膜识别过程中在线更新权值图。权重图通过为每个特征码分配合适的权重来进行虹膜匹配，反映了编码算法在不同虹膜区域上的鲁棒性。通过充分的虹膜模板训练得到的权重图具有收敛性和抗各种噪声的鲁棒性。广泛而全面的实验表明，所提出的个性化虹膜匹配策略比统一的虹膜匹配策略具有更好的虹膜识别性能，特别是对于质量较差的虹膜图像。
Iris recognition typically involves three steps, namely, iris image preprocessing, feature extraction, and feature matching. The first two steps of iris recognition have been well studied, but the last step is less addressed. Each human iris has its unique visual pattern and local image features also vary from region to region, which leads to significant differences in robustness and distinctiveness among the feature codes derived from different iris regions. However, most state-of-the-art iris recognition methods use a uniform matching strategy, where features extracted from different regions of the same person or the same region for different individuals are considered to be equally important. This paper proposes a personalized iris matching strategy using a class-specific weight map learned from the training images of the same iris class. The weight map can be updated online during the iris recognition procedure when the successfully recognized iris images are regarded as the new training data. The weight map reflects the robustness of an encoding algorithm on different iris regions by assigning an appropriate weight to each feature code for iris matching. Such a weight map trained by sufficient iris templates is convergent and robust against various noise. Extensive and comprehensive experiments demonstrate that the proposed personalized iris matching strategy achieves much better iris recognition performance than uniform strategies, especially for poor quality iris images.