# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 5 Conclusions  
This paper investigated a DL framework called FVR-Net for enhancing the recognition accuracy of the finger vein biometric system. The proposed network consists of a block-wise feature extraction network and a classification network. The images input to FVR-Net were first subjected to preprocessing where ROI cropping and image enhancement were performed to segment the vein patterns from the background. Then, the processed image  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kernel Size**} & \multicolumn{2}{c}{**HKPU**} & \multicolumn{2}{c}{**FUVSM**} \\ \cline{2-5}  & **Good Quality** & **Poor Quality** & **Good Quality** & **Poor Quality** \\ \hline
3 \(\times\) 3 & 78.03 & 76.53 & 90.97 & 95.97 \\ \hline
5 \(\times\) 5 & 93.18 & 88.97 & 97.84 & 97.22 \\ \hline
7 \(\times\) 7 & 45.45 & 72.65 & 90.27 & 83.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Impact of Varying Kernel Size on the Recognition Performance of the FVR-Net.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(M\)} & \multicolumn{2}{c}{**HKPU**} & \multicolumn{2}{c}{**FUVSM**} \\ \cline{2-5}  & **Good Quality** & **Poor Quality** & **Good Quality** & **Poor Quality** \\ \hline
3 & 82.57 & 68.775 & 90.97 & 90.94 \\ \hline
4 & 87.12 & 76.73 & 89.23 & 95.4 \\ \hline
5 & 93.18 & 88.97 & 97.84 & 97.22 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Impact of Increasing Depth of the Network on the Recognition Performance of the FVR-Net.  
was fed into the feature extraction network where each block entailed a convolutional layer followed by hybrid pooling of parallelly placed two subsampling layers (maxpooling and average pooling). Their activation maps were concatenated and fed into the classification network with three FCLs for providing the vein recognition output. The whole model was designed to discretely identify an individual by extracting distinct interclass features of the vein image sample, regardless of ambiguity. This was possible through the introduction of hybrid pooling, which not only activated the most discriminative features of the input but also took into account feature localization, which is a very important thing to consider when dealing with vein images that have translational variant features. Extensive experiments on two publicly available datasets demonstrated the effectiveness of our model, because maximum recognition accuracies of up to 97.84% and 97.22% were achieved for good- and poor-quality images, respectively, and the performance improved significantly, compared to previous methods. Further experiments were conducted by varying network hyperparameters such as regularization technique, the size of convolutional kernels, and the network depth, from which we obtained the optimal hyperparameter settings to achieve maximum recognition accuracy. The proposed method provides great potential and insights into the development of finger vein recognition models for future biometric systems.  
Conceptualization, L.D.T. and B.W.K.; methodology, L.D.T. and B.W.K.; formal analysis, L.D.T. and B.W.K.; investigation, L.D.T.; writing--original draft preparation, L.D.T. and B.W.K.; writing--review and editing, L.D.T. and B.W.K.; supervision, B.W.K.; project administration, B.W.K.; funding acquisition, B.W.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a project for Industry-Academic Cooperation Based Platform R&D funded Korea Ministry of SMEs and Startups in 2020 (S3014213) and a National Research Foundation of Korea (NRF) grant funded by the Korean government (2022R1A2B5B01001543).  
Not applicable.  
The authors declare no conflict of interest.