# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
###### Abstract  
This study aims to capture subtle changes in the pupil, identify relatively weak inter-class changes, extract more abstract and discriminative pupil features, and study a pupil refinement recognition method based on attention mechanisms. Based on the deep learning framework and the ResNet101 deep residual network as the backbone network, a pupil refinement recognition model is established. Among them, the image preprocessing module is used to preprocess the pupil images captured by infrared spectroscopy, removing internal noise from the pupil images. By using the ResNet101 backbone network, subtle changes in the pupil are captured, weak inter-class changes are identified, and different features of the pupil image are extracted. The channel attention module is used to screen pupil features and obtain key pupil features. External attention modules are used to enhance the expression of key pupil feature information and extract more abstract and discriminative pupil features. The Softmax classifier is used to process the pupil features captured by infrared spectra and output refined pupil recognition results. Experimental results show that this method can effectively preprocess pupil images captured by infrared spectroscopy and extract pupil features. This method can effectively achieve fine pupil recognition, and the fine recognition effect is relatively good. deep residual network; attention mechanism; pupil recognition; refined identification; Softmax classifier +
Footnote †: journal: applied sciences  
0000-0001-423  
consider the variability in pupil features among different individuals; therefore, the study of pupil refinement recognition is particularly important. At present, pupil recognition technology has made significant progress but still faces many challenges. On the one hand, the quality of a pupil's image is affected by many factors, such as lighting conditions, eye occlusion, camera resolution, etc. These factors may lead to a decline in recognition accuracy. On the other hand, the features of different individuals' pupils are different, such as the complexity of iris texture, changes in pupil shape, etc., increasing the difficulty of feature extraction and matching. In addition, real-time, security, and privacy protection should also be considered in pupil recognition technology. In order to overcome these challenges, researchers have proposed a variety of fine pupil recognition methods. For example, [PERSON], [PERSON] et al. [6] located the inner edge of the iris by gray histogram segmentation and threshold processing and used calculus operators to narrow the search range to locate the outer edge to achieve high-precision iris location. In the feature extraction phase, wavelet transform is used to extract iris texture features. In view of the shortcomings of the existing feature extraction methods, improvements are made in preserving texture regions, extracting feature signals along two directions, and detecting singular points using the modulus maxima of the first derivative. Singularities are judged through the location of modulus maxima after wavelet transform, and feature coding is carried out to finally achieve efficient feature matching and then complete iris recognition. Although wavelet transform improves the abstraction of feature extraction to a certain extent, the extracted features are not sufficient to fully characterize the complexity and uniqueness of the pupil, especially when faced with complex recognition tasks, such as iris recognition under different races, ages, genders, or health conditions. [PERSON], [PERSON] and others [7] first designed an iris segmentation algorithm based on CNN (convolutional neural network) in view of the noise factors such as eyeglasses occlusion, blur, angle deviation, etc. in the iris image. This algorithm effectively improves the positioning information and semantic information fusion of bottom-to-top features and improves the accuracy of iris feature extraction by building a feature pyramid and introducing a bottom-up path.