When compared to unimodal systems, multimodal biometric systems have several advantages, including lower error rate, higher accuracy, and larger population coverage. However, multimodal systems have an increased demand for integrity and privacy because they must store multiple biometric traits associated with each user. In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user’s face and iris biometrics. We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary multimodal shared latent representation. Further, we employ a hybrid secure architecture by combining cancelable biometrics with secure sketch techniques and integrate it with a deep hashing framework, which makes it computationally prohibitive to forge a combination of multiple biometrics that passes the authentication. The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics. Furthermore, the proposed approach also provides cancelability and unlinkability of the templates along with improved privacy of the biometric data. Additionally, we also test the proposed hashing function for an image retrieval application using a benchmark dataset. The main goal of this paper is to develop a method for integrating multimodal fusion, deep hashing, and biometric security, with an emphasis on structural data from modalities like face and iris. The proposed approach is in no way a general biometrics security framework that can be applied to all biometrics modalities, as further research is needed to extend the proposed framework to other unconstrained biometric modalities.

中文翻译：

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用于安全的多模式生物识别的深度哈希

与单峰系统相比，多峰生物识别系统具有多个优势，包括更低的错误率，更高的准确性和更大的人口覆盖率。但是，由于多模式系统必须存储与每个用户相关的多个生物特征，因此对完整性和隐私性的需求增加。在本文中，我们提出了一种用于特征级融合的深度学习框架，该框架从每个用户的面部和虹膜生物特征生成安全的多峰模板。我们将深度哈希（二值化）技术集成到融合体系结构中，以生成健壮的二进制多模式共享潜在表示。此外，我们通过将可取消的生物识别技术与安全草图技术结合在一起，并采用深度哈希框架将其整合在一起，从而采用了一种混合安全架构。这使得在计算上禁止伪造通过身份验证的多个生物特征的组合。使用面部和虹膜的多峰数据库显示了该方法的有效性，并观察到由于多种生物识别技术的融合，匹配性能得到了改善。此外，所提出的方法还提供了模板的可取消性和不可链接性以及生物统计数据的改进的隐私性。此外，我们还使用基准数据集测试了针对图像检索应用程序提出的哈希函数。本文的主要目标是开发一种集成多模式融合，深度哈希和生物识别安全性的方法，重点是来自面部和虹膜等模式的结构数据。