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：

---

用于安全多模式生物识别的深度哈希


与单模态系统相比，多模态生物识别系统具有多种优势，包括错误率更低、准确度更高、人群覆盖范围更大。然而，多模式系统对完整性和隐私性的需求不断增加，因为它们必须存储与每个用户相关的多个生物特征。在本文中，我们提出了一个用于特征级融合的深度学习框架，该框架根据每个用户的面部和虹膜生物特征生成安全的多模态模板。我们将深度哈希（二值化）技术集成到融合架构中，以生成鲁棒的二进制多模态共享潜在表示。此外，我们采用混合安全架构，将可取消的生物识别技术与安全草图技术相结合，并将其与深度哈希框架集成，这使得在计算上无法伪造通过身份验证的多个生物识别技术的组合。使用人脸和虹膜的多模态数据库显示了所提出方法的有效性，并且观察到由于多种生物特征的融合，匹配性能得到了提高。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，以及改进的生物识别数据的隐私性。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态融合、深度哈希和生物识别安全的方法，重点关注面部和虹膜等模态的结构数据。 所提出的方法绝不是可应用于所有生物识别模式的通用生物识别安全框架，因为需要进一步研究以将所提出的框架扩展到其他不受约束的生物识别模式。