# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
###### Abstract
Therefore, using Levenshtein distance directly cannot accurately reflect the real differences between iris images, and it is difficult to capture sufficient distinguishing features. In order to capture subtle changes in the pupils and ensure that the network can recognize relatively weak inter-class changes, deep learning models can automatically learn complex feature representations from large amounts of data [11] and extract more abstract and discriminative pupil features. The pupil refinement recognition method based on a deep residual network and attention mechanism is studied in this paper to improve the accuracy of pupil recognition and provide users with more reliable identity authentication and status monitoring services. This method utilizes the powerful performance of ResNet in the field of image processing, especially its deep structure that can capture more and more detailed feature information, meeting the demand for extracting subtle and complex features in pupil recognition tasks. Subsequently, by introducing attention mechanisms, the network can focus more on the subtle changes and weak inter-class differences in the pupils when extracting features, thereby improving the accuracy and robustness of recognition. This mechanism simulates the attention process in the human visual system, enhancing the network's attention to important features. In addition, combining deep residual networks with attention mechanisms to fully leverage their advantages not only improves the learning efficiency of the network but also makes it more flexible and accurate in processing complex and variable pupil images. This comprehensive application has brought significant performance improvement to the task of fine pupil recognition. In summary, this study proposes a pupil refinement recognition method based on deep residual networks and attention mechanisms, which brings new and original contributions to the current research status of pupil recognition technology. This method is expected to provide users with more reliable identity authentication and status monitoring services in practical applications. Firstly, deep residual networks (ResNet) have demonstrated powerful performance in the field of image processing. By introducing residual connections, ResNet can effectively alleviate the problems of gradient vanishing and exploding during the training process of deep neural networks, enabling the network to learn deeper feature representations. In pupil recognition tasks, it is necessary to extract subtle and complex features from pupil images, and ResNet's deep-level structure can precisely meet this requirement, especially variants such as ResNet101, whose deeper layers and more complex structures enable the network to capture more and more detailed feature information. Secondly, the attention mechanism has made important progress in the field of deep learning in recent years. It simulates the attention mechanism in the human visual system, enabling the network to focus more on important regions and features when processing images. Inpupil recognition, the subtle changes and subtle inter-class differences in the pupil are the focus of attention. By introducing attention mechanisms, the network can focus more on these important changes when extracting features, thereby improving the accuracy and robustness of recognition. In addition, the combination of deep residual networks and attention mechanisms can fully leverage the advantages of both. ResNet provides powerful feature extraction capabilities, while attention mechanisms further enhance the network's attention to important features. This combination not only improves the learning efficiency of the network, but also makes it more flexible and accurate in processing complex and varied pupil images. Finally, selecting these specific machine learning models and algorithms is also based on their performance in practical applications. In previous studies, deep residual networks and attention mechanisms have achieved significant results in multiple image processing tasks.