Despite the ubiquity in the use of biometrics due to its many advantages against traditional methods such as password or token, the emerging cancelable biometric methods, which are designed to protect the biometrics are still exposed to certain threats. Attack via Record Multiplicity (ARM) is one of those. In this paper, we propose a novel framework that possesses two layers of authentication to improve the matching performance of a fingerprint authentication system in the cancelable template setting. In addition, a multi-filter fingerprint matching scheme is devised to deal more effectively with low-quality fingerprint images. Two techniques that are capable of defending against the heinous ARM are also introduced. Security analysis on the system’s capability against the hill-climb attack and pre-image attack is also provided. The proposed scheme has been evaluated over public datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2. It has achieved the best result compared with the state-of-art methods. The source code for this framework is available on demand.

中文翻译：

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用于可取消模板设计的多过滤器指纹匹配框架

尽管由于生物特征识别技术相对于诸如密码或令牌之类的传统方法具有许多优势，生物识别技术的使用无处不在，但旨在保护生物特征识别技术的新兴可取消生物特征识别方法仍面临某些威胁。通过记录多重性（ARM）的攻击就是其中之一。在本文中，我们提出了一种新颖的框架，该框架具有两层身份验证，以在可取消模板设置中提高指纹身份验证系统的匹配性能。另外，设计了多过滤器指纹匹配方案以更有效地处理低质量的指纹图像。还介绍了两种能够抵御令人讨厌的ARM的技术。还提供了针对系统抵抗爬山攻击和映像前攻击的能力的安全性分析。已对公共数据集FVC2002-DB1，FVC2002-DB2，FVC2002-DB3和FVC2004-DB2评估了所提出的方案。与最先进的方法相比，它已达到最佳效果。该框架的源代码可按需提供。