Improve iris classification performance based on AdaBoost

Qi Chuan Tian; Zheng Guang Liu; Lin Sheng Li; Yao Hong Qu; Zi Liang Li; Xi Rong Liu

Improve iris classification performance based on AdaBoost

Qi Chuan Tian, Zheng Guang Liu, Lin Sheng Li, Yao Hong Qu, Zi Liang Li, Xi Rong Liu

自动化学院

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

According to the problem that the iris image resolution difference makes iris recognition difficult, an AdaBoost is proposed to solve the problem. The AdaBoost algorithm will achieve a stronger iris classifier (named iris feature template) by lifting weaker similarity classifiers during the training stage. Simulation results show that the algorithm can improve iris classification performance and it is very easy for iris classification threshold selection in iris recognition.

源语言	英语
页（从-至）	4045-4048+4053
期刊	Xitong Fangzhen Xuebao / Journal of System Simulation
卷	20
期	15
出版状态	已出版 - 5 8月 2008

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title = "Improve iris classification performance based on AdaBoost",

abstract = "According to the problem that the iris image resolution difference makes iris recognition difficult, an AdaBoost is proposed to solve the problem. The AdaBoost algorithm will achieve a stronger iris classifier (named iris feature template) by lifting weaker similarity classifiers during the training stage. Simulation results show that the algorithm can improve iris classification performance and it is very easy for iris classification threshold selection in iris recognition.",

keywords = "Adaptive boosting algorithm, Feature template, Iris classification, Machine learning, Pattern recognition",

author = "Tian, {Qi Chuan} and Liu, {Zheng Guang} and Li, {Lin Sheng} and Qu, {Yao Hong} and Li, {Zi Liang} and Liu, {Xi Rong}",

year = "2008",

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AU - Tian, Qi Chuan

AU - Liu, Zheng Guang

AU - Li, Lin Sheng

AU - Qu, Yao Hong

AU - Li, Zi Liang

AU - Liu, Xi Rong

PY - 2008/8/5

Y1 - 2008/8/5

N2 - According to the problem that the iris image resolution difference makes iris recognition difficult, an AdaBoost is proposed to solve the problem. The AdaBoost algorithm will achieve a stronger iris classifier (named iris feature template) by lifting weaker similarity classifiers during the training stage. Simulation results show that the algorithm can improve iris classification performance and it is very easy for iris classification threshold selection in iris recognition.

AB - According to the problem that the iris image resolution difference makes iris recognition difficult, an AdaBoost is proposed to solve the problem. The AdaBoost algorithm will achieve a stronger iris classifier (named iris feature template) by lifting weaker similarity classifiers during the training stage. Simulation results show that the algorithm can improve iris classification performance and it is very easy for iris classification threshold selection in iris recognition.

KW - Adaptive boosting algorithm

KW - Feature template

KW - Iris classification

KW - Machine learning

KW - Pattern recognition

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M3 - 文章

AN - SCOPUS:50049115048

SN - 1004-731X

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JO - Xitong Fangzhen Xuebao / Journal of System Simulation

JF - Xitong Fangzhen Xuebao / Journal of System Simulation

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ER -

Improve iris classification performance based on AdaBoost

摘要

其它文件与链接

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引用此