Review of Deep Learning Based Iris Recognition

Abstract

The highly accurate, secure, and stable biometric technology of iris recognition is well-known. The current iris recognition system shows stable performance under the condition of constraints on user status and acquisition equipment, but it cannot adapt to the current complex and diverse open scenes. Open scenarios contain a large number of uncertain acquisition factors, for example, iris images acquired in open scenarios are easily interfered by factors such as eyelashes, hair blockage, and specular reflection, etc. These uncertainties often lead to an overall decline in image quality, resulting in a significant decline in the performance of iris image segmentation and feature extraction. In recent years, deep learning algorithms have been widely used in iris recognition, aiming to improve the adaptability of the system to open scenarios. The current status of the application of deep learning technology in iris recognition is reviewed, and its key role in improving recognition accuracy in open scenarios is summarized. Firstly, the background of iris recognition is presented. Secondly, the performance of various deep learning models in iris segmentation, iris feature extraction and feature matching tasks is analyzed, and their advantages and limitations are expounded. Then, the common iris datasets and their characteristics are systematically summarized. Lastly, new challenges and potential directions for future exploration of iris recognition are pointed out.

Keywords

• iris recognition; biometrics; pattern recognition; computer vision; deep learning