An intelligent method for iris recognition using supervised machine learning techniques. (December 2019)

• Record Type: Journal Article
• Authors: Ahmadi, Neda; Nilashi, Mehrbakhsh; Samad, Sarminah; Rashid, Tarik A.; Ahmadi, Hossein
• Is Part Of: Optics & laser technology. Volume 120(2019)
• Publication Date: 2019-12
• Subjects: Iris recognition -- Two dimensional Gabor kernel -- Step filtering -- Polynomial filtering -- Radial basis function neural networks -- Genetic algorithm; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2019.105701
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: A new method is proposed for iris recognition. The method is developed by neural network and genetic algorithm. The method is evaluated on CASIA-Iris V3 and UCI machine datasets. Abstract: In the new millennium, with chaotic situation that exist in the world, people are threaten with multifarious terrorist attacks. There have been several intelligent ways in order to recognize and diminish these assaults wisely. Biometric traits have proven to be one of the useful ways for tackling these problems. Among all these traits, the iris recognition systems are appropriate tools for the human identification not only the iris pattern is well-known features, but also it has numerous features such as compactness representation, uniqueness texture, and stability. In spite of the fact that there have been published many approaches in these areas, there are still abundant problems in these approaches like time consuming, and computational complexity. In order to solve these obstacles, we propose the extravagant iris recognition methods that are based on combination of two dimensional Gabor kernel (2-DGK), step filtering (SF) and polynomial filtering (PF) for feature extraction and hybrid radial basis function neural network (RBFNN) with genetic algorithm (GA) for matching task. To assess the performance of the proposed method, we use two benchmarks in our algorithm and implemented it on CASIA-Iris V3, UBIRIS. V1 and UCI machine learning repository datasets. The experimental results of the proposed method reveal that the method is efficient in the iris recognition.