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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Random permutation principal component analysis for cancelable biometric recognition
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Random permutation principal component analysis for cancelable biometric recognition

机译：可取消的生物识别识别随机置换主成分分析

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摘要

Although biometrics is being increasingly used across the world, it also raises concerns over privacy and security of the enrolled identities. This is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. To address these problems, in this paper, we suggest two simple and powerful techniques called (i) Random Permutation Principal Component Analysis (RP-PCA) and (ii) Random Permutation Two Dimensional Principal Component Analysis (RP-2DPCA). The proposed techniques are based on the idea of cancelable biometric which can be reissued if compromised. The proposed techniques work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The identity of a person is recognized only if the combination of template and PIN is valid, otherwise the identity is rejected. The superiority of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The key advantages of the proposed techniques are (i) classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation (ii) robustness across different biometrics. In addition, no image registration is required for performing recognition.
机译：虽然生物识别学越来越多地用于世界各地，但它也提出了令人关切的隐私和招收身份的安全性。这是由于生物识别性无法取消，如果受到损害可能可以访问入侵者。为了解决这些问题，在本文中，我们建议推出（i）随机置换主成分分析（RP-PCA）和（ii）随机排列二维主成分分析（RP-2DPCA）的两个简单强大的技术。所提出的技术基于可取消的生物识别的思想，如果泄露，可以重新发出。所提出的技术通过接受被取消的生物识别模板和向用户发出的密钥（称为引脚）以密码方式工作。仅当模板和引脚的组合有效时，才会识别人的身份，否则拒绝身份。提出的技术的优越性在三个可自由的面部（ORL），虹膜（Ubiris）和耳朵（IITD）数据集上进行了演示，用于针对最先进的方法。所提出的技术的关键优势是（i）分类精度仍未受到由于使用在不同生物识别性上的随机置换（ii）鲁棒性产生的可被取消的生物识别模板而受到影响。此外，执行识别不需要图像注册。

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