# Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning
## 3 Experiments
### CFP Face Database Clustering Test  
We use 5000 images in the CFP Face Database for the clustering test. We mainly search for the number of clusters automatically, because as the number of identities in the data increases, the number of required features also continue to increase, as shown in Figure 7.  
We randomly select 50, 100, 200, 300, 400 and all human images in the dataset for adaptive clustering, and compare the F-score value calculated by the clustering results with the k-means algorithm. As shown in Table 3, the clustering accuracy of our algorithm is still higher than the k-means algorithm, and it can automatically search for the appropriate number of clusters K. As the number of clusters increases, there is still some deviation between the number of searched clusters K and the actual number of people. The images with incorrect clustering are presented separately, as shown in Figure 10. There is a huge age gap between different images of the same person, leading to morphological differences and making clustering errors. The algorithm judges young images into different categories to increase the number of clusters. However, the untagged images acquired through video surveillance, medical equipment, and social media over a period of time are basically the same age. The deviation in the number of clusters caused by age changes is not a big problem. Figure 11 shows the path for searching on the CFP Face Database of 5000 images. The number of clusters is large, so the first step is 20 for rough searching, but it is difficult to converge in the final result. Because a large step size will cause a large change in the number of clusters, and then a large change in the _DBI_ value. However, the optimal domain range of the number of clusters 450, 580 can be obtained through a rough search. Then, the final optimal number of clusters is 532 through an exact search with a step size of two. Although there are some deviations, it proves that the clustering parameter adaptive algorithm we designed can still obtain a suitable number of clusters. The F-score calculated for the final clustering result is 0.7483, which is better than 0.4367 obtained by the k-means algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Number**} & \multirow{2}{*}{**Clustering Number K**} & \multicolumn{2}{c}{**F-Score**} \\ \cline{3-4}  & & **Ours** & **K-Means** \\ \hline
50 & 50 & 0.9912 & 0.7756 \\
100 & 100 & 0.9775 & 0.6458 \\
200 & 206 & 0.8357 & 0.5026 \\
300 & 314 & 0.7750 & 0.4647 \\
400 & 420 & 0.7563 & 0.4421 \\
500 & 532 & 0.7483 & 0.4367 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clustering test results for the CFP Face Database. Figure 10: The image clustering performance at different ages. We also tested the method on the LFW face dataset [28], which contained 13,233 images of 5749 people. First, we deleted the images that contained multiple faces in the same image. The 12,323 images of the remaining 5484 people are clustered, and the final cluster number K is 5622, and the F-score is 0.6492. Because the data samples provided in the LFW dataset are not balanced (the number of images for each person is different), the clustering accuracy is not high. We conducted a test on the YouTube Faces Dataset [29]. First, the video was intercepted into images at 20-frame intervals, and images containing multiple faces and no faces were removed through face detection. While obtaining enough face images, we also ensured that there would be a certain amount of deflection angle, illumination, and other changes between the images of the same person. In the end, we constructed a dataset of 27,650 images containing 1595 people. Using the proposed approach to cluster the dataset, the final cluster number K is 1684, and the F-score is 0.7724. Although the number of images increases, accuracy is better than the results achieved on the CFP dataset, because the changes between images of the same person captured in a video are small.