Abstract
Iris recognition is a kind of important biometrics technology for personal identify verification, iris classification method has been achieved more attention according to different feature extraction. Binary feature extraction is one of the most effective techniques employed for the human iris recognition problem. However, the selection of a particular set of features is often problematic, so iris classifier performance isn’t satisfied. Based on AdaBoost, an enhanced algorithm for iris classifier is presented in this paper. The algorithm will achieve a stronger iris classifier (iris feature template) by lifting weaker similarity classifiers based on AdaBoost through training samples. Simulation results on CASIA iris database show that the method is effective.
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Tian, QC., Zhao, XL., Wu, XJ., Li, LS., Liu, L. (2007). Iris Classifier Enhanced Algorithm Based on AdaBoost. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_112
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DOI: https://doi.org/10.1007/978-3-540-74282-1_112
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