## 3 Experiments
### Face Recognition Results
The PF07 database contained the images of 200 subjects (100 males and 100 females) of Asian ethnic groups, all of whom provided four images with different facial expression conditions (i.e., neutral, angry, smiling, and surprised). The Yale database included 15 subjects (14 males and a female) of many different races. We used four facial expressions (i.e., neutral, sad, smiling, and surprised), excluding those with eyes closed or winking (Table 3). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Bosphorus**} & \multicolumn{2}{c}{**RaFD**} \\ \cline{2-5}  & **PCA** & **DCV** & **PCA** & **DCV** \\ \hline B-WIM & **95.56\%** & **96.11\%** & **90.48\%** & **93.33\%** \\ WIM [31] & 91.11\% & 89.44\% & 84.29\% & 90.95\% \\ ICR [28] & 93.89\% & 71.67\% & 76.67\% & 59.05\% \\ EPC(Y)A2+ [21] & 92.78\% & 84.44\% & 65.71\% & 65.24\% \\ SPCA+ [25] & 88.89\% & 85.00\% & 55.71\% & 65.71\% \\ (2D)2 PCA [26] & 91.11\% & 92.78\% & 76.19\% & 75.71\% \\ SLC [27] & 91.11\% & 83.89\% & 75.24\% & 71.90\% \\ MVI [29] & 92.78\% & 90.56\% & 74.76\% & 75.24\% \\ SRGES [30] & 92.22\% & 82.78\% & 77.62\% & **93.33**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Face recognition results in _Closed Set_ protocol. Figure 6: Face recognition results for a various number of DCV features in “_Closed Set_” protocol. Table 4 shows the face recognition results with a separate auxiliary set. Because the auxiliary set was constructed from separate databases, the face recognition experiment used images of all the subjects contained in each database. Depending on the degree of variation, the differences in face recognition rates were measured in the order of the proposed method: (5.77%), SRGES (8.76%), WIM (10.37%), SLC (10.69%), (2D)\({}^{2}\)PCA (15.38%), MVI (16.87%), ICR (17.38%), E(PC)\({}^{2}\)A2+ (24.77%), and SPCA+ (30.83%). For the criteria, the proposed method and SLC maintained high performance, whereas the remaining methods showed differences in face recognition performance. Generally, the proposed method showed the highest face recognition rates. Figure 7 shows the recognition rates for a different number of DCV features. The proposed method gives a recognition rate of 88.51% with 57 features and 87.21% with 66 features for the Bosphorus and RaFD databases, respectively. As can be seen from Figure 7, the proposed method shows the best recognition performance compared to the other methods for all number of features. This experiment also confirmed the superiority of the proposed method for each criterion. On the other hand, from the results of Tables 2 and 4, it can be seen that the recognition rate in the \"_Closed Set_\" protocol was about 10% higher than that in the \"_Open Set_\" protocol. It is generally known that face recognition performance decreases as the number of subjects to be recognized increases [48]. In our experiment, however, we think the main reason for the difference between the results of Tables 2 and 4 is that, in the \"_Closed Set_\" protocol, the images included in the auxiliaries set had homogeneous characteristics, because they were taken under similar conditions of resolution, camera type, lighting conditions, etc. However, in the \"_Open Set_\" protocol, the auxiliary set comprised images from various kinds of databases, which differ from the query images.