# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
## 4 Conclusions  
The pupil, as a part of human biometric features, has a unique texture and shape. Through the refined recognition method, the pupil's fine features can be captured more accurately so as to improve the overall accuracy of biometrics. Therefore, research is conducted on a pupil refinement recognition method based on deep residual networks and attention mechanisms. This article uses the deep residual network ResNet101 as the backbone network for feature extraction, combined with the image preprocessing module, which can effectively process the pupil images captured by infrared spectra. It can effectively process the pupil images captured by infrared spectra, not only removing the internal noise of the images but also capturing the subtle changes and weak inter-class differences of the pupils through the powerful feature extraction ability of ResNet101. Furthermore, by introducing the channel attention module and external attention module, this method can filter out key features of the pupil and enhance their information expression, thereby extracting more abstract and discriminative pupil features. Finally, the Softmax classifier was used to process these features, achieving fine-grained recognition of the pupil. Although the method in this paper has achieved remarkable results, there are still some challenges and problems to be solved. Future research can be carried out from the following aspects:  
1. [leftmargin=*]
2. Algorithm optimization and efficiency improvement: In response to the complexity and computational requirements of current deep learning models, we will further optimize the model structure and parameters to improve training efficiency and recognition speed. By exploring more efficient feature extraction and fusion methods, efforts will be made to reduce computational complexity and improve the real-time performance of algorithms to meet the fast response requirements in practical application scenarios. 3. Multi-source data fusion: In order to further improve the accuracy and robustness of pupil recognition, consideration will be given to combining other biometric information (iris texture, corneal reflection, etc.) with pupil information for multi-source data fusion. By integrating multiple biological features and fully utilizing their complementarity, the overall performance of the recognition system is improved, enhancing its adaptability to complex environments. 4. Cross-scene adaptability: In response to various lighting conditions, posture changes, and occlusion situations encountered in practical applications, more flexible pupil recognition algorithms will be studied. By introducing adaptive learning mechanisms or transfer learning methods, the model can adaptively adjust according to the data distribution in different scenarios, thereby maintaining good recognition performance in different scenarios. This will help broaden the application scope of pupil recognition technology and improve its practicality and reliability in practical scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy/\%** & **Recall/\%** & **F1 Score** \\ \hline Iris recognition method used in this paper & 98.5 & 99.0 & 98.8 \\ \hline Pupil detection method based on & 95.0 & 96.0 & 95.5 \\ Hough transform & 96.5 & 97.5 & 97.0 \\ \hline Pupil refinement recognition method based on & & & \\ deep residual network and attention mechanism & 92.0 & 88.0 & 90.0 \\ (without channel attention module) & & & \\ \hline Pupil refinement recognition method based on & & & \\ deep residual network and attention mechanism & 93.0 & 89.0 & 91.0 \\ (without external attention module) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the performances of different methods. In summary, this study has achieved preliminary results in fine pupil recognition, but further efforts are still needed to overcome existing challenges and promote the sustained development of technology.