University of Bahrain
Scientific Journals

A Robust Iris Recognition Approach Based on Transfer Learning

Show simple item record HATTAB, Abdessalam BEHLOUL, Ali 2023-03-02T16:11:12Z 2023-03-02T16:11:12Z 2023-03-02
dc.identifier.issn 2210-142X
dc.description.abstract Iris texture is one of the most secure biometric characteristics used for person recognition, where the most significant step in the iris identification process is effective features extraction. Deep Convolutional Neural network models have been achieved massive success in the features extraction field in recent years, but several of these models have tens to hundreds of millions of parameters, which affect the computational time and resources. A lot of systems proposed in the iris recognition field extract features from normalized iris images after applying many pre-processing steps. These steps affect the quality and computational efficiency of these systems; also, occlusion, reflections, blur, and illumination variation affect the quality of features extracted. This paper proposed a new robust approach for iris recognition that locates the iris region based on the YOLOv4-tiny, then it extracts features without using iris images’ pre-processing, which is a delicate task. In addition, we have proposed an effective model that accelerated the feature extraction process by reducing the architecture of the Inception-v3 model. The obtained results on four benchmark datasets validate the robustness of our approach, where we achieved average accuracy rates of 99.91%, 99.60%, 99.91%, and 99.19% on the IITD, CASIA-Iris-V1, CASIA-Iris-Interval, and CASIA-Iris-Thousand datasets, respectively. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Iris recognition, Deep Learning (DL), Transfer Learning (TL), Convolutional Neural Network (CNN), Pre-trained Inception-v3, YOLOv4-tiny en_US
dc.title A Robust Iris Recognition Approach Based on Transfer Learning en_US
dc.type Article en_US
dc.identifier.doi en
dc.contributor.authoraffiliation LaSTIC laboratory, Computer science department, University of Batna 2, Batna, Algeria en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

Files in this item

This item appears in the following Issue(s)

Show simple item record

All Journals

Advanced Search


Administrator Account