# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 4 Experiments and Results
### Impact of Regularization Techniques  
As aforementioned in Section 3.2, the original architecture of the proposed model employs mainly two types of regularization techniques, L2 regularization and dropout in feature extraction and classification. The former removes small percentage of the weights at each iteration of training while the latter randomly drops neural units and their connection during training. Both techniques have equal importance while training deep neural network and serve a mutual purpose of reducing the risk of overfitting. In this section, we conduct experiments to examine how the proposed model performs in the presence of one or more regularization techniques. Impact of L2 Regularization and Dropouts  
First, we considered removing the L2 regularization from the feature extraction network during training and observed the effects on recognition performance during inference. For this, the number of convolutional layers, subsampling layers, filters, and their sizes in fully convolutional feature extraction were not changed. The dropout penalty used in classification was also kept intact throughout the training; however, the L2 regularization was set with a 0 penalty. In other words, no regularization was utilized in feature extrac \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Recognition Accuracy (\%)**} \\ \cline{3-4}  & & **Good Quality** & **Poor Quality** \\ \hline \multirow{2}{*}{P-SVM [42]} & HKPU & 80.57 & 79.04 \\ \cline{2-4}  & FVUSM & 66.48 & 65.71 \\ \hline \multirow{2}{*}{NN [43]} & HKPU & 81.44 & 81.82 \\ \cline{2-4}  & FVUSM & 62.35 & 55.00 \\ \hline \multirow{2}{*}{DNN [32]} & HKPU & 84.59 & 83.64 \\ \cline{2-4}  & FVUSM & 69.33 & 68.57 \\ \hline \multirow{2}{*}{FVR-Net} & HKPU & **93.18** & **88.97** \\ \cline{2-4}  & FVUSM & **97.84** & **97.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of Recognition Accuracy from FVR-Net with Several Baseline Finger Vein Recognition Algorithms. tion. Table 6 shows the comparison of recognition accuracy exhibited by the model when trained with and without L2 regularization and dropout. In most cases, we can see the obvious degradation in the recognition accuracy when L2 regularization is not adopted. With no regularization, the model becomes extremely overfit to the training data, so its recognition performance with the test data became horrendous. Moreover, the absence of a regularization penalty constrains the model's ability to optimize the weight value. Due to this, the network becomes more sensitive to noise in the training data and incurs degraded recognition performance. A good finger vein recognition model must perform well, regardless of the physiological variations in the acquisition environment, i.e., the trained model must generalize well on varying orientations in the test data. Therefore, the introduction of regularization during the finger vein feature extraction is compulsory. Second, we considered removing dropout from the classification network of the proposed model. During the experiment, we analyzed the impact of dropout on recognition performance from FVR-Net. As mentioned, dropout is utilized only in the network's classification process (after FCL-1, and FCL-2). Therefore, by removing dropout, the units present in the corresponding layers remain intact, with active incoming and outgoing connections from and to the neighboring layers. The L2 regularization penalty was kept unchanged in feature extraction. The same table shows the recognition performance from FVR-Net with and without dropout layers during classification. We can see significantly higher recognition accuracy when dropout layers are utilized. While training with dropout, the corresponding neuron of the imminent layer remains deactivated during feedforwarding. Therefore, an arbitrary neuron from the same layer is responsible for making predictions for the missing neuron.