Biometric Authentication Using Invariant Gray Level Co-Щccurrence Matrices (GLCM)

EasyChair Preprint no. 11568

Abstract

Biometric authentication plays a crucial role in ensuring security, access control and identity verification, with applications in various sectors and contexts. Biometric authentication in the context of access control is a resilient security technique employed to oversee and manage entry to physical locations, digital platforms, or valuable assets. It verifies the identities of individuals by assessing their distinct biological or behavioral attributes. This approach ensures that it enhances security and accountability by granting access only to authorized individuals. This article introduces an authentication method centered on retinal images, enhancing security levels through the utilization of the Invariant Gray Level Co-occurrence Matrices (GLCM) technique for feature extraction and employing a Random Forest classifier for classification. Our evaluation, conducted on the RIDB database, yielded highly favorable results, achieving a flawless accuracy of 100%.

Keyphrases: Authentication, GLCM, Random Forest, Retina

@Booklet{EasyChair:11568,
  author = {Mokhtari Aicha and Hadj Slimane Zine-Eddine},
  title = {Biometric Authentication Using Invariant Gray Level Co-Щccurrence Matrices (GLCM)},
  howpublished = {EasyChair Preprint no. 11568},

  year = {EasyChair, 2023}}