# The Effects of AI-Driven Face Restoration on Forensic Face Recognition
## 5 Discussion  
The data pointed out that the blur level has a significant impact on recognition performance. As the blurriness of the image increased, the recognition performance of the system typically decreased. Specifically, blurring increases the noise in the image, making feature extraction more difficult and resulting in an increase in error rates for face recognition. In practice, to avoid the impact of blurring on facial recognition systems, some measures can be taken, such as using better lighting conditions, selecting proper camera positions, and using image processing techniques to improve image quality. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Restoration Method**} & \multicolumn{2}{c}{**Blur Level**} \\ \cline{2-3}  & **B1** & **B2** \\ \hline RST 1 & 96.91 \(\pm\) 1.00 \({}^{\text{b}}\) & 85.64 \(\pm\) 7.27 \({}^{\text{b}}\)A \\ RST 2 & 97.35 \(\pm\) 0.86 \({}^{\text{c}}\) & 89.07 \(\pm\) 6.02 \({}^{\text{a}}\)A \\ RST 3 & 96.44 \(\pm\) 1.24 \({}^{\text{a}}\) & 85.93 \(\pm\) 6.72 \({}^{\text{b}}\)A \\ RST 4 & 96.37 \(\pm\) 2.4 \({}^{\text{a}}\) & 83.36 \(\pm\) 7.02 \({}^{\text{c}}\)A \\ \hline \hline \end{tabular} Note. Different lowercase letters in the table indicate significant differences (\(p\) < 0.05) in recognition performance among different restoration methods under the same blur level factor. Different uppercase letters in the table indicate significant differences (\(p\) < 0.05) in recognition performance among different blur levels under the same restoration method factor.  
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Table 4: Test results of comparison between blur level and restoration method under Baidu. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Restoration Method**} & \multicolumn{2}{c}{**Blur Level**} \\ \cline{2-3}  & **B1** & **B2** \\ \hline RST 1 & 93.49 \(\pm\) 1.93 \({}^{\text{b}}\) & 83.49 \(\pm\) 3.88 \({}^{\text{b}}\)A \\ RST 2 & 94.55 \(\pm\) 1.79 \({}^{\text{c}}\) & 86.18 \(\pm\) 3.09 \({}^{\text{c}}\)A \\ RST 3 & 92.56 \(\pm\) 2.16 \({}^{\text{a}}\) & 84.16 \(\pm\) 2.99 \({}^{\text{b}}\)A \\ RST 4 & 92.28 \(\pm\) 3.05 \({}^{\text{a}}\) & 82.38 \(\pm\) 3.94 \({}^{\text{a}}\)A \\ \hline \hline \end{tabular} Note. Different lowercase letters in the table indicate significant differences (\(p\) < 0.05) in recognition performance among different restoration methods under the same blur level factor. Different uppercase letters in the table indicate significant differences (\(p\) < 0.05) in recognition performance among different blur levels under the same restoration method factor.  
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Table 5: Test results of comparison between blur level and restoration method under Ali Cloud. From the experimental results, it can be seen that the repaired images have not significantly improved facial recognition performance. Sometimes, the inadequacy or failure of the restoration techniques even led to a decrease in recognition performance. The effectiveness of image restoration methods varied significantly, with HitPaw Photo Enhancer and Topaz Photo AI demonstrating superior recognition accuracy. The findings suggest that these algorithms and techniques enhance image restoration by more accurately capturing intricate details and features. Nonetheless, the distinctions among various restoration methods are often subtle. Thus, in practical applications, it is essential to carefully choose the appropriate restoration method tailored to the specific data characteristics and application requirements. Moreover, it is vital to conduct a thorough validation and assessment of the recognition system's outcomes to significantly reduce the risk of misinterpretations and errors. Finally, in practical applications, the selection of blur level and restoration method should be tailored to specific factors like application context and data quality. In summary, both the blur level and restoration method had a significant impact on the recognition performance, and some interactions collectively affected recognition performance.