FDFNet: A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing

Avantika Singh, Ashish Arora, Shreyal Patel, Gaurav Jaswal, A. Nigam
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引用次数: 4

Abstract

Present world has already been consistently exploring the fine edges of online and digital world by imposing multiple challenging problems/scenarios. Similar to physical world, personal identity management is very crucial inorder to provide any secure online system. Last decade has seen a lot of work in this area using biometrics such as face, fingerprint, iris etc. Still there exist several vulnerabilities and one should have to address the problem of compromised biometrics much more seriously, since they cannot be modified easily once compromised. In this work, we have proposed a secure cancelable finger dorsal template generation network (learning domain specific features) secured via. Bio-Hashing. Proposed system effectively protects the original finger dorsal images by withdrawing compromised template and reassigning the new one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been proposed for extracting the discriminative features. This network is exclusively trained on trait specific features without using any kind of pre-trained architecture. Later Bio-Hashing, a technique based on assigning a tokenized random number to each user, has been used to hash the features extracted from FDFNet. To test the performance of the proposed architecture, we have tested it over two benchmark public finger knuckle datasets: PolyU FKP and PolyU Contactless FKI. The experimental results shows the effectiveness of the proposed system in terms of security and accuracy.
FDFNet:一个安全的可取消的深指背模板生成网络。Bio-Hashing
当今世界已经通过施加多种具有挑战性的问题/场景,不断探索在线和数字世界的精细边缘。与现实世界类似,为了提供安全的在线系统,个人身份管理非常重要。在过去的十年中,在这一领域使用生物识别技术进行了大量的工作,如面部、指纹、虹膜等。但是仍然存在一些漏洞,人们应该更认真地解决生物识别信息泄露的问题,因为它们一旦被泄露就不能轻易修改。在这项工作中,我们提出了一个安全的可取消的手指背模板生成网络(学习特定领域的特征)。Bio-Hashing。该系统通过提取受损模板并重新分配新的模板,有效地保护了原始手指背图像。提出了一种新的手指-背侧特征提取网络(FDFNet)来提取识别特征。该网络专门针对特定的特征进行训练,而不使用任何预训练的架构。后来的生物哈希,一种基于给每个用户分配一个标记化随机数的技术,已经被用来对从FDFNet中提取的特征进行哈希。为了测试该架构的性能,我们在两个基准的公共手指关节数据集上进行了测试:理大FKP和理大非接触式FKI。实验结果表明了该系统在安全性和准确性方面的有效性。
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