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AES Based Multimodal Biometric Authentication using Cryptographic Level Fusion with Fingerprint and Finger Knuckle Print

机译:基于AES的多模式生物特征认证,使用具有指纹和指节指纹的加密级别融合

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摘要

In general, the identification and verification are done by passwords, pin number, etc., which are easily cracked by others. In order to, overcome this issue biometrics is a unique tool to authenticate an individual person. Biometric is a quantity which consists of an individual physical characteristics of fingerprint, Finger Knuckle Print (FKP), iris, face and so on. These characteristics are not easily cracked by others. Nevertheless, unimodal biometric suffers due to noise, infra class variations, spoof attacks, non-universality and some other attacks. In order to, avoid these attacks, the multimodal biometrics i.e., a combination of more modalities is adapted. They are combined with cryptography, which will give more security for physical characters of biometrics. Bio-crypto system provides the authentication as well as the confidentiality of the data. This paper proposes to improve the security of multimodal systems by generating the biometric key from fingerprint and FKP biometrics with its feature extraction using K-means algorithm. The secret value is encrypted with biometric key using symmetric Advanced Encryption Standard (AES) Algorithm. This paper also, discusses about the integration of fingerprint and FKP using package model cryptographic level fusion in order to improve the overall performance of the system. The encryption process will give more authentication and security for the system. The Cyclic Redundancy Check (CRC) function protects the biometric data from malicious tampering and also it provides error checking functionality.
机译:通常,识别和验证是通过密码,密码等进行的,这些密码,密码容易被他人破解。为了克服这个问题,生物特征识别是验证个人身份的独特工具。生物特征识别是由指纹,指关节指纹(FKP),虹膜,面部等各个物理特征组成的数量。这些特性不容易被他人破解。然而,单峰生物特征遭受噪声,基础设施变化,欺骗攻击,非大学攻击和其他一些攻击的影响。为了避免这些攻击,采用了多模式生物特征,即更多模式的组合。它们与加密技术结合使用,这将为生物识别的物理特征提供更高的安全性。生物加密系统提供数据的身份验证和机密性。本文提出通过利用指纹和FKP生物特征生成生物特征密钥并使用K-means算法提取特征来提高多模式系统的安全性。使用对称高级加密标准(AES)算法使用生物识别密钥对秘密值进行加密。本文还讨论了使用封装模型密码级别融合的指纹和FKP集成,以提高系统的整体性能。加密过程将为系统提供更多的身份验证和安全性。循环冗余校验(CRC)功能可保护生物特征数据免受恶意篡改,并提供错误校验功能。

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