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Green Image
Issue No. 09 - September (2011 vol. 33)
ISSN: 0162-8828
pp: 1744-1757
Wenbo Dong , Institute of Automation, Chinese Academy of Sciences, Beijing
Zhenan Sun , Institute of Automation, Chinese Academy of Sciences, Beijing
Tieniu Tan , Institute of Automation, Chinese Academy of Sciences, Beijing
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.
Iris recognition, Hamming distance, personalized matching strategy, weight map, ordinal features, binominal mixture model.

T. Tan, W. Dong and Z. Sun, "Iris Matching Based on Personalized Weight Map," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1744-1757, 2010.
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