# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
###### Abstract
At the same time, in the mask prediction branching stage, the up-sampling and convolutional block attention module (CBAM) technology is used to further enhance the salient information of the feature map and improve the iris segmentation accuracy. In the iris recognition phase, the pre-trained CNN model is used to extract feature descriptors of iris images. These feature descriptors can represent complex image features and effectively capture the subtle differences in iris texture. By comparing these feature descriptors with iris templates in the database, high-precision iris recognition is achieved. This method mainly relies on CNN to process the iris image of visible light and does not fully consider the spectral characteristics of the pupil at different wavelengths. Spectral-domain information can reveal deeper details and changes in the pupil, but this method has limitations in this respect. [PERSON], [PERSON] and others [8] first normalized and denoised the acquired iris image through preprocessing steps to eliminate the interference of external factors such as illumination and rotation. Then, residual information in the iris image is extracted using ResNet or other deep learning models. The residual image can highlight the tiny differences and feature details in the iris image, providing more abundant and accurate information for subsequent recognition. The statistical-based local texture description method (BSIF) is introduced to further process the residual image. Finally, the machine learning algorithm is used to classify and recognize the extracted feature vectors. By comparing the feature vector of the iris image to be recognized with the feature vector stored in the database, high-precision iris recognition is achieved. The performance of the machine learning algorithm depends on the diversity and quality of training data to a large extent. If the training data does not fully cover the spectral changes or weak inter-class differences in the pupil at different wavelengths, the algorithm will encounter difficulties in identifying these subtle changes. [PERSON], [PERSON] et al. [9], first of all, use preprocessing steps to enhance image quality, including denoising, light equalization, and image enhancement techniques, to improve the reliability of subsequent processing. Then, a local gradient pattern is used to extract texture features of the iris image. In order to further improve the recognition performance, deep learning technology is introduced to further learn and represent the features extracted from the local gradient pattern. Finally, the trained deep learning model is used to classify and make decisions on the extracted features to achieve high-precision recognition of long-distance iris. Although local gradient mode can extract local texture features of images, its sensitivity to subtle texture changes may be limited. In iris images, especially in remote or low-quality images, subtle texture differences are difficult to accurately capture. In addition, the local gradient model focuses more on the statistical characteristics of local regions than on the global or higher-level feature representation. [PERSON], [PERSON] et al. [10] used advanced image processing techniques to extract spatial features of iris images, including local patterns of textures, edge contours, and geometric shapes of iris regions. In the feature comparison stage, Levenshtein distance is introduced as the metric to compare the spatial feature differences between different iris images and achieve iris recognition. Levenshtein distance is mainly used to measure the difference between two strings. It calculates the minimum editing times through insertion, deletion, and replacement operations. However, in iris recognition, it is not entirely appropriate to apply Levenshtein distance to the measurement of spatial features. The spatial features of iris images (such as texture, edge, etc.) are usually not simple string forms, but complex image data.