# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 5 Experimental Analysis
### Performance Analysis of Multi-Instance Feature Descriptors Biometrics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Fingers** & **Matchers** & **Orientations** & **Pixels** & **Cells** & **Norm** & **Transform\_sqrtt** & **EER** \\ \hline ('li’,) & BSBF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 13.57\% \\ ('li’,) & BSBF-linear2 & 9 & 18 & 1 & L2 & No & 9.09\% \\ ('li’,) & BSBF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 13.57\% \\ ('li’,) & BSBF-rbf2 & 9 & 18 & 1 & L2 & No & 9.09\% \\ ('li’,) & BSFSF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 3.60\% \\ ('li’,) & BSFSF-linear2 & 14 & 23 & 2 & L1-sqrt & Yes & 1.72\% \\ ('li’,) & BSSF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 5.42\% \\ ('li’,) & BSSF-rbf2 & 19 & 28 & 3 & L1-sqrtt & No & 2.19\% \\ ('li’, 'lm’) & BSBF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 6.04\% \\ ('li’, 'lm’) & BSBF-linear2 & 19 & 30 & 1 & L1-sqrtt & No & 3.71\% \\ ('li’, 'lm’) & BSBF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 5.56\% \\ ('li’, 'lm’) & BSBF-rbf2 & 20 & 25 & 1 & L2 & Yes & 3.61\% \\ ('li’, 'lm’) & BSSF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 0.83\% \\ ('li’, 'lm’) & BSSF-linear2 & 14 & 14 & 1 & L2 & No & 0.39\% \\ ('li’, 'lm’) & BSSF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 2.03\% \\ ('li’, 'lm’) & BSSF-rbf2 & 17 & 29 & 3 & L1-sqrtt & No & 0.22\% \\ ('li’, 'lm', 'lr') & BSBF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 5.00\% \\ ('li’, 'lm', 'lr') & BSBF-linear2 & 7 & 30 & 1 & L2 & No & 2.64\% \\ ('li’, 'lm', 'lr') & BSBF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 4.74\% \\ ('li’, 'lm', 'lr') & BSBF-rbf2 & 5 & 30 & 1 & L2 & Yes & 1.84\% \\ ('li', 'lm', 'lr') & BSSF-linear1 & 9 & 8 & 3 & L2-Hys & Yes & 0.47\% \\ ('li', 'lm', 'lr') & BSSF-linear2 & 8 & 27 & 1 & L2 & No & 0.31\% \\ ('li', 'lm', 'lr') & BSSF-rbf1 & 9 & 8 & 3 & L2-Hys & Yes & 1.67\% \\ ('li’, 'lm', 'lr') & BSSF-rbf2 & 7 & 27 & 2 & L1-sqrtt & No & 0.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of the proposed system for UTFVP. Figure 7: ROC curve of the proposed system for SDUMLA-HMT. Table 1 shows the EER for the different matching techniques with optimizations using the SDUMLA-HMT dataset. It can be observed that BSSF with RBF kernel, in general, gives the best result. The lowest EER for the left index finger (li) alone is 1.35%. The fusion of the left index and middle fingers is able to attain better performance with an EER of 0.77%, although it is slightly inferior to the linear kernel (_BSSF-linear2_) at 0.58%. The best performance can be achieved by combining the left index, middle, and ring fingers with the lowest EER of 0.48% in the RBF kernel. As compared to the BSSF approach, the performance of BSBF is slightly poorer with higher EER for both linear and RBF Kernel. The best results of the BSBF approach are with an EER of 6.44% with RBF kernel by combining the three finger vein instances. The EER for the UTFVP dataset is presented in Table 2. It can be observed that the performance of the proposed methods tested using different optimization settings for different finger vein combinations is presented. It can be observed that _BSSF-linear2_, in general, gives better results with the EER of 1.72% for the left index finger. The lowest EER that combines the left index, middle, and ring fingers is 0.31% for _BSSF-linear2_. Amongst the results, BSSF-_rb/2_ gives the best EER of 0.22% when using the left index and middle finger. Similar to Table 1, the BSSF method outperforms BSBF in terms of EER. The lowest EER achieved for the BSBF method is 1.84% by using the RBF kernel. To conclude, both the SDUMLA-HMT and UTFVP datasets are able to attain the best performance by using the BSSF approach with RBF kernel, with EERs as low as 0.48% and 0.22%, respectively.