# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 4 Experiments and Results
### Identification Performance  
After successfully training the network, a performance evaluation of the proposed finger vein recognition network was carried out on the test set. As mentioned in Section III-C, two images from each class were allocated for testing purposes to demonstrate the recognition performance of the proposed model. During the evaluation, preprocessed images of test samples from each dataset were used as input to FVR-Net to obtain the corresponding probability scores. To evaluate the performance of the proposed model, we compared the recognition accuracy of FVR-Net with simple P-SVM [42], a neural network (NN) [43], and a state-of-the-art CNN technique [32]. P-SVM is a simple ML-based classifier that is stacked on top of hand-crafted features to generate the probability scores of the corresponding class. Similarly, the NN scheme [42] consists of a single hidden layer and was trained with stochastic gradient descent (SGD). In contrast, the deep neural network (DNN)-based scheme [43] is a multilayer CNN with nine layers (six convolutional layers and three FCLs), which also utilizes the P-SVM classifier at the end to generate the probability of each input belonging to the respective class. The implementation of these studies was not released online, and therefore, the results obtained by these conventional schemes were kept the same as when presented in [32]. Table 5 presents the recognition accuracy obtained by FVR-Net compared with various conventional schemes. We can see that, for good-quality images from the HKPU dataset, the proposed method obtained 12.61%, 11.74%, and 8.59% higher accuracy than the P-SVM, NN, and DNN schemes, respectively. With the FVUSM dataset, recognition accuracy with the proposed method was about 31.36%, 35.49%, and 28.51% higher than the aforementioned schemes. The exquisite recognition performance exhibited by the proposed model can be explained by its robust feature extraction network. The vein patterns of the good-quality images were concisely distributed over the entire spatial location of the image, and thus, subjecting them to the robustly designed feature extraction network activates the most abstract representation of the input at each layer. With distinctive features extracted for each subject, the classification layer is able to accurately classify the image to recognize an individual. Figure 5: Learning curves of the proposed model on training and validation datasets. (**a**) Loss vs. Epochs, and (**b**) Accuracy vs. Epochs. Our proposed model exhibited significantly higher accuracy for low-quality images as well. With the HKPU dataset, we witnessed 9.93%, 7.15%, and 5.33% higher recognition accuracy, whereas with FVUSM, we saw 31.15%, 42.22%, and 28.95% higher accuracy than the baseline P-SVM, NN, and DNN models, respectively. Note that the image acquisition period for HKPU is longer than that of FVUSM, and more intraclass variation exists, leading to more mismatching errors [32]. In addition, because of the thick and accumulated vein patterns in FVUSM, the model can produce more robust activation maps than HKPU, where the vein structures are thinner and more scattered. Since the proposed method makes the best effort to extract the finger vein feature information as much as possible, it shows higher accuracy for the FVUSM dataset, where the within-class variation is slightly lower than that of HKPU. In addition to that, since the vein patterns associated with poor-quality images are extremely vague in nature, the infirmly designed feature extraction network of the existing models cannot extract distinctive features from them, making it exceedingly difficult to obtain higher recognition accuracy. In contrast, the feature extraction network of the proposed model can capture large and ambiguous instances of the input by using substantially larger kernels present in the primary layers. Due to this, a noteworthy accumulation of features at the final layer can be realized, which is sufficient to accurately classify every sample, regardless of its visual quality.