# Evaluation of a Vein Biometric Recognition System on an Ordinary Smartphone
## 5 Discussion  
In the context of biometric authentication systems for access control, the use of an app for ordinary smartphones is very interesting to increase the usability (collectability) and acceptability of the system. While facial and fingerprint recognition has been widely employed in smartphones, the use of vein-based traits has not yet been sufficiently studied and exploited. Custom-designed devices with near-infrared light sources usually perform vein acquisition in vascular biometric systems. Some of these designed devices may be expensive and cannot be used in some contexts because of their portability and size. In this work, we have explored the feasibility of an app, the free IRVeinViewer app, that detects superficial veins from images captured in the visible spectrum by an ordinary smartphone camera. Since deep veins are not well acquired, we designed and evaluated our system with a database composed of 1000 images of dorsal hands and wrists since they have more superficial veins and are easier to acquire with a smartphone. Among the feature-extraction algorithms reported for vein-based recognition, we employed the maximum curvature (MC) and SIFT (scale-invariant feature transform) to evaluate in terms of the equal error rate (EER) the authentication performance obtained. The results show that SIFT algorithm provides better EER values than MC and that wrist images allow better authentication than images of dorsal hands. Using SIFT on wrist images, the EER values range from 0.63% to 16.80%. The system implemented in Android allows for performance in real time, with the feature extraction stage being the slowest stage (it lasts 127.62 ms). The best and worst EER values with our database are similar to those obtained with databases whose images were acquired with specific devices. Hence, it is worth further improving the cheaper solution explored herein. The influence of environmental factors in the authentication performance was evaluated considering acquisitions performed outdoors and indoors (with natural and artificial light or no light). The results show that indoor acquisitions are somewhat better than outdoor acquisitions. However, our conclusion is that how the user acquires the images with the app from one time to another has a greater influence, i.e., if the user carries out matching in the same environment as enrollment, and after a short time from enrollment, the authentication is better. Therefore, one way of improving this work is to develop a smart app that guides the user in the acquisition. We will consider this as future work. The methods employed in the literature for the quality estimation of images did not detect images with unclearly acquired veins. Hence, it is worth exploring other methods \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Body Part**} & **EER (\%)** & **EER (\%)** & \multirow{2}{*}{**\% Removed**} \\  & & **without Quality** & & **with Quality** \\  & & **Control** & & **Control** \\ \hline \multirow{3}{*}{Outdoor} & \multicolumn{1}{c}{Wrist} & 5.50 & 1.10 & 7 \\  & Dorsal Hand & 3.76 & 1.15 & 9 \\ Indoor natural & \multicolumn{1}{c}{Wrist} & 0.63 & 0.50 & 1 \\ light 1 & Dorsal Hand & 2.87 & 0 & 9 \\ Indoor natural & \multicolumn{1}{c}{Wrist} & 1.00 & 0 & 1 \\ light 2 & Dorsal Hand & 0.50 & 0.37 & 5 \\ Indoor artificial & \multicolumn{1}{c}{Wrist} & 2.82 & 0 & 3 \\ light & \multicolumn{1}{c}{Dorsal Hand} & 1.70 & 0.53 & 4 \\  & \multicolumn{1}{c}{Wrist} & 1.43 & 0.78 & 2 \\  & \multicolumn{1}{c}{Dorsal Hand} & 5.80 & 2.87 & 6 \\ All & \multicolumn{1}{c}{Wrist} & 16.80 & 15.45 & 2.8 \\  & \multicolumn{1}{c}{Dorsal hand} & 19.30 & 18.34 & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recognition performance after removing samples by genuine score-based evaluation. such as the genuine score-based evaluation considered in this work. This is something to further explore in a smart app.