# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
## 3 Experimental Analysis
The recognition rates of known and unknown class samples are 88.0% and 78.0%, respectively, with an average recognition rate of 83.0% and a false recognition \begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & \multicolumn{2}{c}{**Fine Identification Results**} \\ \hline \multirow{3}{*}{1} & Pupil center position, pixel coordinates (X, Y) & (500, 300) \\  & Pupil diameter & The 6.5 mm \\  & Pupil shape parameter & Circularity 0.98 \\ \hline \multirow{3}{*}{2} & Pupil center position, pixel coordinates (X, Y) & (505, 302) \\  & Pupil diameter & The 6.3 mm \\  & Pupil shape parameter & Circularity 0.97 \\ \hline \multirow{3}{*}{3} & Pupil center position, pixel coordinates (X, Y) & (498, 295) \\  & Pupil diameter & The 6.8 mm \\  & Pupil shape parameter & Circularity 0.97 \\ \hline \multirow{3}{*}{4} & Pupil center position, pixel coordinates (X, Y) & (510, 308) \\  & Pupil diameter & The 6.2 mm \\  & Pupil shape parameter & Circularity 0.95 \\ \hline \multirow{3}{*}{5} & Pupil center position, pixel coordinates (X, Y) & (490, 301) \\  & Pupil diameter & The 6.7 mm \\  & Pupil shape parameter & Circularity 0.98 \\ \hline \multirow{3}{*}{6} & Pupil center position, pixel coordinates (X, Y) & (515, 298) \\  & Pupil diameter & The 6.4 mm \\  & Pupil shape parameter & Circularity 0.96 \\ \hline \multirow{3}{*}{7} & Pupil center position, pixel coordinates (X, Y) & (485, 310) \\  & Pupil diameter & The 6.9 mm \\  & Pupil shape parameter & Circularity 0.99 \\ \hline \multirow{3}{*}{8} & Pupil center position, pixel coordinates (X, Y) & (520, 305) \\  & Pupil diameter & The 6.1 mm \\  & Pupil shape parameter & Circularity 0.94 \\ \hline \multirow{3}{*}{9} & Pupil center position, pixel coordinates (X, Y) & (492, 292) \\  & Pupil diameter & The 6.6 mm \\  & Pupil shape parameter & Circularity 0.97 \\ \hline \multirow{3}{*}{10} & Pupil center position, pixel coordinates (X, Y) & (507, 312) \\  & Pupil diameter & The 6.5 mm \\  & Pupil shape parameter & Circularity 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fine pupil recognition results. rate of 12.0%. This indicates that the residual image method performs well in processing iris images with subtle differences. Compared with the method proposed in this article, the residual image method still has a gap in recognition performance, especially in the recognition of unknown class samples. The iris recognition method based on local gradient pattern extracts the local gradient pattern of the iris image to construct feature vectors, which have high recognition accuracy. The recognition rates of known and unknown class samples are 92.0% and 82.0%, respectively, with an average recognition rate of 87.0% and a false recognition rate of 8.0%. This indicates that the local gradient mode method has advantages in processing iris images with significant texture changes. Although the local gradient pattern method performs well, compared with the method proposed in this paper, there is still room for improvement in the recognition of unknown class samples and the average recognition rate and recognition stability are slightly lower. The Levenshtein distance-based iris recognition method uses Levenshtein distance to measure the similarity between iris features and has a certain recognition effect. However, the recognition rates of known and unknown class samples were 86.0% and 76.0%, respectively, with an average recognition rate of 81.0% and a false recognition rate of 14.0%. This indicates that the Levenshtein distance method has limited effectiveness in processing iris images with significant deformation. Compared with the method proposed in this article, the Levenshtein distance method has a significant gap in recognition performance, especially in the recognition of unknown class samples (88.0% vs. 76.0%), and the false recognition rate and recognition stability are also poor. This method achieves fine recognition of iris images by optimizing feature extraction and classification strategies.