Feature Selection for Iris Recognition with AdaBoost

Kan-Ru Chen, Chia-Te Chou, S. Shih, Wen-Shiung Chen, Duan-Yu Chen
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引用次数: 10

Abstract

In this paper, we proposed a method for selecting edge-type features for iris recognition. The AdaBoost algorithm is used to select a filter bank from a pile of filter candidates. The decisions of the weak classifiers associated with the filter bank are linearly combined to form a strong classifier. Real experiments have been conducted to assess the performance of the designed strong classifier. The results showed that the boosting algorithm can effectively improve the recognition accuracy at the cost of slightly increase the computation time.
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基于AdaBoost的虹膜识别特征选择
本文提出了一种用于虹膜识别的边缘类型特征选择方法。AdaBoost算法用于从一堆候选滤波器中选择滤波器组。将与滤波器组相关联的弱分类器的决策线性组合形成强分类器。实际实验已经进行了评估所设计的强分类器的性能。结果表明,增强算法可以有效地提高识别精度,但代价是计算时间略有增加。
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