Abstract
Binarized statistical image features (BSIF) have been successfully used for texture analysis in many computer vision tasks, including iris recognition and biometric presentation attack detection. One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images. This paper tests the question of whether domain-specific BSIF can give better performance than the default BSIF. The second important point is in the selection of image patches to use in training for BSIF. Can image patches derived from eye-tracking experiments, in which humans perform an iris recognition task, give better performance than random patches? Our results say that (1) domain-specific BSIF features can out-perform the default BSIF features, and (2) selecting image patches in a task-specific manner guided by human performance can out-perform selecting random patches. These results are important because BSIF is often regarded as a generic texture tool that does not need any domain adaptation, and human-task-guided selection of patches for training has never (to our knowledge) been done. This paper follows the reproducible research requirements, and the new iris-domain-specific BSIF filters, the patches used in filter training, the database used in testing and the source codes of the designed iris recognition method are made available along with this paper to facilitate applications of this concept.
Abstract (translated)
二值统计图像特征（BSIF）已经成功地用于许多计算机视觉任务中的纹理分析，包括虹膜识别和生物特征呈现攻击检测。一个重要的一点是BSIF在虹膜识别中的所有应用都使用了原始的BSIF滤波器，这些滤波器是在从自然图像中提取的图像块上进行训练的。本文测试了特定于域的BSIF是否能提供比默认BSIF更好的性能的问题。第二个重点是选择用于BSIF训练的图像补丁。可以从人眼执行虹膜识别任务的眼动追踪实验中获得的图像补丁比随机补丁提供更好的性能吗？我们的结果表明：（1）特定于域的BSIF特征可以胜过默认的BSIF特征，以及（2）以人类表现为指导的以任务特定的方式选择图像补丁可以胜过选择随机补丁。这些结果很重要，因为BSIF通常被认为是一种通用的纹理工具，不需要任何域适应，人工任务指导的训练补丁选择从未（据我们所知）完成。本文遵循可重复的研究要求，新的虹膜域专用BSIF滤波器，滤波器训练中使用的补丁，测试中使用的数据库和设计的虹膜识别方法的源代码随本文一起提供，以方便应用这个概念。
URL
https://arxiv.org/abs/1807.05248
