Feature Encoding and Selection for Iris Recognition Based on Variable Length Black Hole Optimization

Abstract

Iris recognition as a biometric identification method is one of the most reliable biometric human identification methods. It exploits the distinctive pattern of the iris area. Typically, several steps are performed for iris recognition, namely, pre-processing, segmentation, normalization, extraction, coding and classification. In this article, we present a novel algorithm for iris recognition that includes in addition to iris features extraction and coding the step of feature selection. Furthermore, it enables selecting a variable length of features for iris recognition by adapting our recent algorithm variable length black hole optimization (VLBHO). It is the first variable length feature selection for iris recognition. Our proposed algorithm enables segments-based decomposition of features according to their relevance which makes the optimization more efficient in terms of both memory and computation and more promising in terms of convergence. For classification, the article uses the famous support vector machine (SVM) and the Logistic model. The proposed algorithm has been evaluated based on two iris datasets, namely, IITD and CASIA. The finding is that optimizing feature encoding and selection based on VLBHO is superior to the benchmarks with an improvement percentage of 0.21%.