A Model for Authentication System Using Iris Detection in Cloud Computing

Lenu Godwin; V.I. E Anireh; Daniel matthias  Email

doi:10.56201/ijasmt.v10.no3.2024.pg59.67

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A Model for Authentication System Using Iris Detection in Cloud Computing

Lenu Godwin, V.I. E Anireh & Daniel matthias Email

Abstract

Various techniques are employed to address security concerns in the realm of cloud computing. Password authentication is widely recognised as one of the most often employed methods of authentication. Many clients choose to use easily memorable options, such as phone numbers and names, as their chosen passwords. The passwords in question possess a high degree of memorability yet are susceptible to security breaches. Consequently, the attacker possesses the capability to systematically compile a comprehensive inventory of prominent identities and numerical values, so compromising the integrity of the security measures in place. The primary aim of this paper is to develop a model for an authentication system that utilises iris detection within the context of cloud computing. The iris recognition system is comprised of two main processes, namely the registration process and the verification process. During the enrollment process, the feature vector that has been extracted is placed in the database. During the verification process, the system compared the feature set with each of the feature sets retrieved from the training image to determine the closest match with a training set image. During the process of matching, the feature that has been extracted is compared to the features that have been saved. The classification task has been performed using the Non-Symmetrical Support Vector Machine. Experiments conducted using live captured images. The findings indicate that the system operates with high efficiency and achieves a classification rate of 98% correctness.

Keywords

Authentication SystemSecurityIris Detection

References

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A Model for Authentication System Using Iris Detection in Cloud Computing

Abstract

Keywords

References

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