A selective feature information approach for iris image-quality measure
A selective feature information approach for iris image-quality measure
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DOI：
10.1109/tifs.2008.924606
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发表时间：
2008-09-01
影响因子：
6.8
通讯作者：
Du, Yingzi
中科院分区：
文献类型：
--
作者：
Belcher, Craig;Du, Yingzi
质量差的图像会严重影响虹膜识别系统的准确性，因为它们没有足够的特征信息。然而，现有的质量度量关注的是参数或因素，而不是特征信息。可用于测量的特征的质量是虹膜区域的独特性和可用虹膜区域的数量的组合。一些虹膜可能只有一小部分变化的图案。因此，该方法自动选择变化模式最明显的虹膜部分来度量特征信息。闭塞和扩张的组合决定了可用的虹膜区域的数量，并被考虑在拟议的质量度量中。质量分数是特征信息分数、遮挡分数和膨胀分数的融合结果。使用二维Gabor和一维Log-Gabor小波方法对质量分数和识别精度之间的关系进行了评估，并使用不同的数据集进行了验证。并与卷积矩阵法、谱能量法和墨西哥帽小波法进行了比较。这三种方法代表了虹膜质量测量的各种方法。实验结果表明，提出的质量分数与识别精度高度相关，能够对识别结果进行预测。
Poor quality images can significantly affect the accuracy of iris-recognition systems because they do not have enough feature information. However, existing quality measures have focused on parameters or factors other than feature information. The quality of feature available for measure is a combination of the distinctiveness of the iris region and the amount of iris region available. Some irises may only have a small area of changing patterns. Due to this, the proposed approach automatically selects the portions of the iris with the most distinguishable changing patterns to measure the feature information. The combination of occlusion and dilation determines the amount of iris region available and is considered in the proposed quality measure. The quality score is the fused result of the feature information score, the occlusion score, and the dilation score. The relationship between the quality score and recognition accuracy is evaluated using 2-D Gabor and 1-D Log-Gabor wavelet approaches and validated using a diverse data set. In addition, the proposed method is compared with the convolution matrix, spectrum energy, and Mexican hat wavelet methods. These three methods represent a variety of approaches for iris-quality measure. The experimental results show that the proposed quality score is highly correlated with the recognition accuracy and is capable of predicting the recognition results.