Iris Texture Recognition Using Co-occurence Matrix Features with K_means Algorithm | Zendy

Azhar M. Kadim | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Iris Texture Recognition Using Co-occurence Matrix Features with K_means Algorithm

Author(s) -

Azhar M. Kadim

Publication year - 2011

Publication title -

journal of al-nahrain university-science

Language(s) - English

Resource type - Journals

eISSN - 2519-0881

pISSN - 1814-5922

DOI - 10.22401/jnus.14.4.27

Subject(s) - iris recognition , biometrics , iris (biosensor) , computer science , authentication (law) , texture (cosmology) , co occurrence matrix , identification (biology) , artificial intelligence , pattern recognition (psychology) , image (mathematics) , matrix (chemical analysis) , computer vision , algorithm , image texture , image processing , computer security , botany , materials science , composite material , biology

Iris Recognition is a rapidly expanding method of biometric authentication that is well suited to be applied to any access control system requiring high level of security. In this paper k-means algorithm is employed to optimize the database enrollment, this is carried out by choosing the best image (among many) for the same person to be a template in the database. Iris images are mapped into texture features produced from co-occurrence matrix. Experimental results show that the performance of the proposed recognition system gave true identification rate of about 86% when using optimized database and 59% when using selected database.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research