# Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm
## 5 Conclusions  
Traditional facial recognition methods based on feature extraction have many problems, such as being vulnerable to attackers, requiring a large amount of training data and computational resources, etc. Therefore, this study proposes a DL-based FR method that uses DBSCAN to preprocess noisy datasets. This can provide high-quality datasets to train the model. Moreover, based on CDCNN, improvements were made to propose the DS-CDCN algorithm. The experimental data indicated that the loss value of the DBSCAN algorithm was less than 0.1, and the loss value of KMCA and hierarchical clustering algorithm were both greater than 0.2. In image denoising, adaptive median filtering had a better effect, with an average signal-to-noise ratio of 33.36 dB and an average image structural similarity of 0.92. The daytime FI enhancement improved the contrast and brightness of the image by approximately 24.5% and 19.8%, respectively. After the nighttime FI enhancement, the images' contrast and brightness improved by approximately 36.9% and 26.9%, respectively. To ensure the best performance of the algorithm, the shared weight hyperparameter was set to 0.5. The DS-CDCN algorithm achieved a lower average CER compared to CDCNN and SDNet by 2.49% and 17.01%, respectively. It was able to accurately recognize and differentiate between different classes of samples. The results indicate that the method exhibits higher accuracy and robustness, and can fulfill the FR requirements for real-time and security. The limitation of this research is that the DBSCAN algorithm performs well on small and medium-sized datasets but requires more time and computational resources when dealing with large-scale datasets. Therefore, future research can explore and study DL-based face clustering algorithms more to improve the efficiency and accuracy of large-scale face clustering.  
Conceptualization, N.D.; Methodology, N.D.; Software, Z.X. and X.L.; Formal analysis, N.D. and C.G.; Investigation, N.D.; Resources, X.L.; Data curation, Z.X., C.G. and X.W.; Writing--original draft, N.D.; Visualization, N.D. and X.W.; Project administration, Z.X.; Funding acquisition, N.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the R&D investment intensity growth reward fund from the Science and Technology Bureau of Chengde City, Hebei Province, with project number 232304B. This work is supported by the school-level fund of Hebei Minzu Normal University, with fund number DR2023003.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Face Classification** & **Algorithm** & **Error Rate 1 (\%)** & **Error Rate 2 (\%)** & **Error Rate 3 (\%)** \\ \hline \multirow{4}{*}{Africa} & DS-CDCN & 5.28 & 1.33 & 3.31 \\  & CDCNN & 13.8 & 1.56 & 7.68 \\  & SDNet & 35.6 & 15.8 & 25.71 \\  & DenseNet & 22.7 & 8.2 & 9.1 \\  & FaceNet & 16.3 & 5.3 & 6.7 \\ \hline \multirow{4}{*}{Asia} & DS-CDCN & 6.39 & 1.43 & 3.91 \\  & CDCNN & 8.96 & 1.69 & 5.33 \\  & SDNet & 15.67 & 12.36 & 14.02 \\  & DenseNet & 24.5 & 9.3 & 9.9 \\  & FaceNet & 17.3 & 6.8 & 7.3 \\ \hline \multirow{4}{*}{Europe} & DS-CDCN & 12.31 & 2.69 & 7.51 \\  & CDCNN & 14.73 & 3.64 & 9.19 \\ \cline{1-1}  & SDNet & 45.62 & 6.45 & 26.04 \\ \cline{1-1}  & DenseNet & 30.3 & 13.6 & 14.7 \\ \cline{1-1}  & FaceNet & 20.3 & 18.7 & 19.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Commonly used large-scale face image datasets.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.