Efficient IRIS Recognition through Improvement of Feature Extraction and subset Selection

TL;DR

This paper enhances iris recognition by improving feature extraction with contourlet transform, selecting significant bits, and using SVM classification, resulting in faster processing and higher accuracy.

Contribution

It introduces a novel feature subset selection method using contourlet transform and significant bit extraction for improved iris recognition.

Findings

Reduced feature vector size compared to other methods

Increased classification accuracy

Decreased processing time

Abstract

The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. The deterministic feature sequence is extracted from the iris image by using the contourlet transform technique. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. And finally we use SVM (Support Vector Machine) classifier for approximating the amount of people identification in our proposed system. Experimental result show that most proposed method reduces processing time and increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.