# 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
### Validation of the Effect of Face Recognition Method Based on DS-CDCN Algorithm  
To confirm the efficacy and dependability of the FR approach founded on the DS-CDCN algorithm, the research develops a whole experimental setup, comprising hardware and software. The hardware adopts an Intel Core i7-9700K processor, NVIDIA GeForce RTX 3090 graphics card, and a 1 TB NVMe solid-state drive for storing data and parameters. The experiment uses Ubuntu 18.04.5 LTS operating system, TensorFlow 2.5 DL framework, Python 3.8 compiler, and commonly used libraries such as NumPy and Pandas. The research is equipped with a Logitech C920 camera (San Jose, CA, USA) for capturing FIs. Before transferring the image to the computer, it is necessary to preprocess the image to eliminate noise and lighting effects. The preprocessed image should be transferred to the computer via a wireless network and stored in the database. One-third of the data from the homemade image dataset was used for the ablation experiments on the convolution parameters. To compare the impact of convolution parameters on the convolution results, ten values are chosen at random for the tests. The algorithm's performance is assessed using the real presentation classification error rate (CER) and average CER. To avoid the randomness of experimental results, tests 1-5 are repeated under the same experimental conditions. Figure 9 illustrates how the convolution settings affect the algorithm's performance. In Figure 9, as the shared weight, i.e., the hyperparameter is \(\theta\). The algorithm's true presentation CER and average CER show more significant changes. When the hyperparameter is less than 0.2, the true presentation CER and average CER gradually increase. When the hyperparameter is greater than 0.2 and less than 0.5, the true presentation CER and average CER gradually decrease. When the hyperparameter is greater than 0.5, the real presentation CER and average CER gradually increase. Combining the above analysis and experimental data, the value of the shared weight, i.e., the hyperparameter \(\theta\), is set to 0.5 to  
Figure 8: The data information of the enhanced image data. Figure 9: The impact of convolution parameters on algorithm performance. guarantee the best performance of the algorithm. The recognition effect of the DS-CDCN algorithm on the faces of different races is shown in Figure 10.  
In Figure 10, the true rendering error of the DS-CDCN algorithm for African, Asian and European FI recognition is less than 3%. This shows that the algorithm has high accuracy and robustness in recognizing faces of different races. The DS-CDCN algorithm is able to achieve fast and accurate FR with a low false recognition rate in FIs of different races. This indicates that the DS-CDCN algorithm is an advanced algorithm for FR of different races, and can provide efficient and accurate services for FR requirements of various races. To further validate the performance of the DS-CDCN algorithm, the study uses CDCNN, spatial detection network (SDNet), DenseNet and FaceNet as a comparison. The comparison of the recognition effect of different algorithms is shown in Table 2.  
In Table 2, the misidentification rates 1-3 denote the attack presentation CER, the true presentation CER and the average CER, respectively. Compared with CDCNN, SDNet, DenseNet and FaceNet, the DS-CDCN algorithm reduces the average CER by 2.49%, 17.01%, 10.9% and 8.2%, respectively, and is able to more accurately recognize and differentiate samples of different categories. In FI recognition in Africa and Asia, the average error of the DS-CDCN algorithm is 3.3% and 3.9%, respectively. In European FI recognition, the average error of the DS-CDCN algorithm is 7.5%. There are certain differences in FI recognition errors among different regions. In the European region, the average error of all algorithms is generally high, which may be related to the diversity and complexity of European FIs.