# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 4 Implementation and Evaluation of the Recognition System
### Implementation Decisions and Performance Results
the knuckles and how the thumb is positioned when closing the hand. This is illustrated in Figure 6 for the case of the MC. Regarding the algorithms, SIFT clearly provides better recognition results than MC. SIFT results are further improved by adjusting the enhancement filters at preprocessing and, mainly, by removing the false matching pairs at postprocessing. An explanation for the better results is that SIFT extracts more information than MC from the images. While MC deals with binary information about the existence or absence of veins, SIFT deals with enriched information summarized in the selection of keypoints. Interestingly, most of the keypoints matched between genuine samples are located in areas of no veins, which is something expected since the areas with no veins are wider. This is shown in Table 3.  
Since SIFT provides better recognition results, the version with the best preprocessing and postprocessing was selected for our Android implementation. For that purpose, OpenCV in native code (C/C++) was used. The computation times provided are shown in Table 4. The feature extraction is the slowest stage (127.62 ms) while the preprocessing (10.35 ms), matching (4.16 ms), and postprocessing (3.37 ms) are much faster. In global terms, the implementation is suitable for real-time recognition. In comparison to other results from the literature, the implementation performed in this work provides lower computation times (despite considering an additional postprocessing stage). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Condition** & **Body Part** & **Vein (\%)** & **No Vein (\%)** \\ \hline \multirow{3}{*}{Outdoor} & \multicolumn{1}{c}{Wrist} & 0.14 & 0.86 \\  & Dorsal Hand & 0.17 & 0.83 \\ \multirow{3}{*}{Indoor natural light 1} & \multicolumn{1}{c}{Wrist} & 0.09 & 0.91 \\  & Dorsal Hand & 0.13 & 0.87 \\ \multirow{3}{*}{Indoor natural light 2} & \multicolumn{1}{c}{Wrist} & 0.13 & 0.87 \\  & Dorsal Hand & 0.14 & 0.86 \\ \multirow{3}{*}{Indoor artificial light} & \multicolumn{1}{c}{Wrist} & 0.07 & 0.93 \\  & Dorsal Hand & 0.10 & 0.90 \\ \multirow{3}{*}{Darkness} & \multicolumn{1}{c}{Wrist} & 0.08 & 0.93 \\  & Dorsal Hand & 0.10 & 0.90 \\ \multirow{3}{*}{**All**} & \multicolumn{1}{c}{Wrist} & 0.15 & 0.85 \\  & Dorsal Hand & 0.17 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SIFT keypoints matched in genuine samples located in vein and no-vein areas. Figure 6: Maximum Curvature features (**right**) extracted from a vein image acquired under indoor natural light 2 condition (**left**). Features outside the ROI are indicated by red ellipses. Concerning the selection of quality assessment algorithms, Table 5 shows the results obtained with our database and the results obtained in the literature. The rows associated with our database are filled in light gray and the highest values are depicted with bold fonts. Overall, these results suggest that these algorithms evaluate the quality of the vein images acquired in this work as comparable to other vein images from databases available in the literature. The problem is that none of these algorithms allowed us to automatically eliminate the blurred images with unclearly detected veins that provoke bad recognition rates. The reason for this is that the quality metrics evaluated by these algorithms are independent of the content and structures of the veins. In order to improve the quality of the acquisition process, we find it more efficient to acquire several images subsequently at both enrollment and matching, and to evaluate their similarity directly with the selected SIFT algorithm, prior to storing their features as templates or comparing them with templates (which is practical since the computation time allows real-time operation). Since the similarity score for a zero false match rate (FMR0) can be known a priori, only those images whose similarity score with the other acquired images are greater than FMR0 are finally considered for both enrollment and matching.