Bin-based classifier fusion of iris and face biometrics
Bin-based classifier fusion of iris and face biometrics
复制标题
DOI：
10.1016/j.neucom.2016.10.048
复制
发表时间：
2017-02
期刊：
影响因子：
6
通讯作者：
Di Miao;Man Zhang;Zhenan Sun;T. Tan;Zhaofeng He
中科院分区：
文献类型：
--
作者：
Di Miao;Man Zhang;Zhenan Sun;T. Tan;Zhaofeng He
准确性和可用性是多生物识别系统的两个最重要的问题。大多数多生物特征识别系统是基于多个生物特征的匹配分数或特征。然而，大量的身份信息是从捕获的多模态生物特征数据中提取分数或特征的过程中丢失的，并且信息的丢失阻止了多生物特征系统的准确性和可用性达到更高的水平。人们认为，匹配分数可以恢复一些身份信息，这在以前的融合工作中没有利用。针对这一问题，本文提出了一种基于面元的多生物特征融合分类方法框架。该方法通过基于二进制的分类器将匹配分数嵌入到一个高维空间中，在这个新的空间中恢复隐藏在匹配分数中的丰富的身份信息。恢复的信息足以更准确地区分冒名顶替者和真正的用户。因此，基于这种丰富信息的多生物特征识别系统能够实现更准确和可靠的结果。然后使用集成学习方法来选择最强大的嵌入空间。在CASIA-Iris-Distance上的实验结果证明了该融合框架的优越性。
Accuracy and usability are the two most important issues for a multibiometric system. Most of multibiometric systems are based on matching scores or features of multiple biometric traits. However, plenty of identity information is lost in the procedure of extracting scores or features from captured multimodal biometric data, and the loss of information stops accuracy and usability of the multibiometric system from reaching a higher level. It is believed that matching scores can recover some identity information, which has not been utilized in previous fusion work. This study proposes a framework of bin-based classifier method for the fusion of multibiometrics, to deal with this problem. The proposed method embeds matching scores into a higher-dimensional space by the bin-based classifier, and rich identity information, which is hidden in matching scores, is recovered in this new space. The recovered information is sufficient to distinguish impostors from genuine users more accurately. Therefore, the multibiometric systems which are based on such rich information, are able to achieve more accurate and reliable results. The ensemble learning method is then used to select the most powerful embedding spaces. Experimental results on the CASIA-Iris-Distance demonstrate the superiority of the proposed fusion framework.