# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 3 The Proposed Model
### Classification  
To learn a nonlinear combination of the features obtained by the feature extraction process, a set of three FCLs are used in the classification block. The output of final concatenation layer C5 of the feature extraction network is flattened to a one-dimension (1D) vector before passing it as input to this block. A detailed architecture of this network is presented in Table 2. Because all input units of an FCL have separate weights associated with each output unit, the total number of learnable parameters is relatively high compared to CNNs. Therefore, to prevent overfitting due to the large number of parameters and to improve generalization of the network, dropouts are exploited after each layer except the final one. The first two layers of the classification network use ReLU, while the last layer uses SoftMax as an activation function. The final layer produces a 1D vector output, which consists of the probabilities that the input data belong to one of the \(S\) classes (_S_ being the total number of subjects providing sample vein images in the dataset). Among the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer** & **Input** & **Activation** & **Regularization** & **Filter** & **Kernel Size** & **Output** \\ \hline \multirow{2}{*}{Conv-1} & Preprocessed image & \multirow{2}{*}{ReLU} & \multirow{2}{*}{L2 (0.0001)} & \multirow{2}{*}{128} & \multirow{2}{*}{(5, 5)} & \multirow{2}{*}{(100, 300, 128)} \\  & (100 \(\times\) 300 \(\times\) 1) & & & & & \\ \hline MaxP-1 & Conv-1 & - & - & - & (2, 2) & (50, 150, 128) \\ \hline \multirow{2}{*}{AvgP-1} & Conv-1 & - & - & - & (2, 2) & (50, 150, 128) \\ \hline \multirow{2}{*}{C1} & Concatenate (MaxP-1, & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{(50, 150, 256)} \\  & AvgP-1) & & & & & \\ \hline Conv-2 & C-1 & ReLU & L2 (0.0001) & 64 & (5, 5) & (50, 150, 64) \\ \hline MaxP-2 & Conv-2 & - & - & - & (2, 2) & (25, 75, 64) \\ \hline AvgP-2 & Conv-2 & - & - & - & (2, 2) & (25, 75, 64) \\ \hline \multirow{2}{*}{C2} & Concatenate (MaxP-2, & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{(25, 75, 128)} \\  & AvgP-2) & & & & & \\ \hline Conv-3 & C-2 & ReLU & L2 (0.0001) & 128 & (5, 5) & (25, 75, 128) \\ \hline MaxP-3 & Conv-3 & - & - & - & (2, 2) & (12, 37, 128) \\ \hline AvgP-3 & Conv-3 & - & - & - & (2, 2) & (12, 37, 128) \\ \hline \multirow{2}{*}{C3} & Concatenate (MaxP-3, & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{(12, 37, 256)} \\  & AvgP-3) & & & & & \\ \hline Conv-4 & C-3 & ReLU & L2 (0.0001) & 64 & (3, 3) & (12, 37, 64) \\ \hline MaxP-4 & Conv-4 & - & - & - & (2, 2) & (6, 18, 64) \\ \hline AvgP-4 & Conv-4 & - & - & - & (2, 2) & (6, 18, 64) \\ \hline \multirow{2}{*}{C4} & Concatenate (MaxP-4, & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{(6, 18, 128)} \\  & AvgP-4) & & & & & \\ \hline Conv-5 & C-4 & ReLU & L2 (0.0001) & 128 & (3, 3) & (6, 18, 128) \\ \hline MaxP-5 & Conv-5 & - & - & - & (2, 2) & (3, 9, 128) \\ \hline AvgP-5 & Conv-5 & - & - & - & (2, 2) & (3, 9, 128) \\ \hline \multirow{2}{*}{C5} & Concatenate (MaxP-5, & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{(3, 9, 256)} \\  & AvgP-5) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed Network Hyperparameters of the Feature Extraction Procedure. The abbreviated terminologies in the table are: Conv: Convolution, MaxP: Maxpool, AvgP: Averagepool, C: Concatenation.  
obtained probabilities, the highest value of the \(S\) classes are selected for the final finger vein identification.