Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm

Abstract

Iris recognition has become an important tool for human authentication. An efficient and robust iris recognition model based on sparse representation using compressive sensing and k-nearest subspace (segments) has been proposed; k-nearest subspace approach is used for short listing the classes to reduce the time. The shortlisted candidates are divided into sectors and the sparse recognition is applied to each sector. Three classifiers: k-nearest distance classifier, Sector based classifier and Cumulative Sparse Concentration Index (CSCI) based classifiers have been used. An additive function based classifier combination scheme has been adopted in which each classifier is associated with a weight. Genetic algorithm is used to learn the weight of each of the classifier. Results obtained on different databases show that the scheme is highly robust with FAR almost zero.