A Robust Iris Recognition Approach Based on Transfer Learning

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.