# Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm
## 4 Application Analysis of Face Recognition Method Based on DS-CDCN Algorithm
### Face Data Preprocessing Validation  
Larger FI datasets usually consist of thousands to millions of FIs, which cover a wide range of different face features, expressions, poses, and lighting conditions. Such large-scale datasets are important for training FR models and help the models learn a wider and more diverse range of face features. The commonly used large-scale FI datasets are shown in Table 1.  
The experiment employs a public dataset for data enhancement processing, whereby the FI obtained from crawling and video is clustered by the DBSCAN algorithm to create a self-built dataset comprising a total of 775,590 images. The experiment will compare the hierarchical clustering algorithm with the K-means clustering algorithm (KMCA) to confirm the clustering effect of the DBSCAN algorithm. Figure 6 compares the various techniques for the image clustering effect.  
In Figure 6a, the three algorithms converge gradually with the increase in FI, and the DBSCAN algorithm has the fastest convergence speed. After convergence, the loss value of the DBSCAN algorithm is less than 0.1, and the loss value of the KMCA and the hierarchical clustering algorithm is greater than 0.2. When compared to KMCA and the hierarchical clustering technique, the DBSCAN approach in Figure 6b has the lowest running time, about 41 s, and saves the most time, 43.66% and 51.22%, respectively, when there are 8000 images. In conclusion, the DBSCAN method works well in terms of computational efficiency and time savings and has a superior clustering impact. The comparison of image denoising effect is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Dataset Name** & \
\begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Characters** \\ \end{tabular} & **State** \\ \hline
1 & WDref & 99,800 & 3000 & Public \\
2 & CASIA WebFace & 494,500 & 10,580 & Public \\
3 & CACD & 163,450 & 2000 & Public \\
4 & VGG-Face & 2,600,000 & 2600 & Public \\
5 & MS-CELEB-1M & 10,000,000 & 100,000 & Public \\
6 & SFC & 4,400,000 & 4000 & Unpublished \\
7 & CelebFaces+ & 2,600,000 & 2600 & Unpublished \\ \hline \hline \end{tabular}
\end{table}
Table 1: Commonly used large-scale face image datasets.  
In Figure 7, adaptive median filtering has a better effect on image denoising, with an average signal-to-noise ratio of 33.36 dB, and an average image structural similarity of 0.92. This indicates that adaptive median filtering can effectively eliminate the noise in FI and retain the structural information of the image. Specifically, adaptive median filtering is a filtering method that combines mean filtering and median filtering by adaptively adjusting the filtering parameters according to the pixel density around the pixel to achieve a better denoising effect. By retaining the image's structural information and effectively removing noise from the image, this approach can raise the image's quality. Therefore, the image filtering in the subsequent experiments uses the adaptive median filtering method approach. The data information of the image after image data enhancement is shown in Figure 8.  
In Figure 8, the contrast and brightness of the image are improved by about 24.5% and 19.8%, respectively, after daytime FI enhancement. The image's contrast and brightness are enhanced by roughly 36.9% and 26.9%, respectively, following nighttime FI enhancement. The outcomes demonstrate that the image enhancement method significantly improves the quality of the images. An image's visual impact can be enhanced, making it easier to see and distinguish by increasing its contrast and brightness. In practical applications, this is of great significance in the fields of FR, image processing and analysis.  
Figure 6: DBSCAN algorithm clustering effect.  
Figure 7: Comparison of image denoising effects.