Representing fingerprint templates in binary form can provide outstanding merits compared to the conventional minutiae-based fingerprint recognition system. The existing fixed-length fingerprint feature extraction methods either suffer from redundant feature magnitude or lack of template security. In this letter, we present a compact (128 bytes) and cancelable fingerprint binary codes generation scheme which enables accurate and efficient comparison as well as high security. This binary representation is also available for advanced encryption schemes (e.g., fuzzy commitment). Specifically, a kernel learning-based real-valued fingerprint feature is converted into compact and cancelable binary code via one permutation hashing. A partial Haar transform is deployed to further strengthen the irreversibility of the whole system. Experimental results on six benchmark datasets FVC2002 and FVC2004 coupled with security analysis demonstrate the superiority of the proposed method compared with several state-of-the-arts.

中文翻译：

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通过一次排列哈希生成紧凑且可取消的指纹二进制代码


与传统的基于细节的指纹识别系统相比，以二进制形式表示指纹模板可以提供突出的优点。现有的固定长度指纹特征提取方法要么存在特征量冗余，要么缺乏模板安全性。在这封信中，我们提出了一种紧凑（128字节）且可取消的指纹二进制码生成方案，该方案能够实现准确高效的比对以及高安全性。这种二进制表示也可用于高级加密方案（例如，模糊承诺）。具体来说，基于内核学习的实值指纹特征通过一次排列哈希转换为紧凑且可取消的二进制代码。部署部分哈尔变换以进一步增强整个系统的不可逆性。六个基准数据集 FVC2002 和 FVC2004 的实验结果与安全性分析相结合，证明了所提出的方法与几种最先进的方法相比的优越性。