## 4 Experiments
### Ablation Experiments  
To verify that the proposed network model can effectively extract the fusion features of FV and IKP, we perform ablation experiments based on different modalities and different fusion methods.  
First, to verify the effectiveness of the proposed fusion method, we compare the proposed fusion method with another simple splicing fusion method. Specifically, the features of the two modalities are only fused once at the output end of the last fully connected Inception-Resnet-v1 by concatenation operation. We concatenate the 512-dimensional output features of FV and the 512-dimensional output features of IKP to form 1024-dimensional features. We denote a Dual-Branch-Net with only stage 4 connections as Dual-Branch-Net (only stage 4 fusion). We present the ROC curve of the experiment in Figure 6 and the precise EER value in Table 3. The ROC curve of the Dual-Branch-Net (only stage 4 fusion) method shifts to the lower right compared with that of the Dual-Branch-Net. This indicates that the recognition performance of the simple-fusion method is worse than that of the proposed fusion method. The EER value based on the Dual-Branch-Net (only stage 4 fusion) in Table 3 indicates that the corresponding EER values vary by 0.377%. There is one possible cause for the deterioration of the performance of Dual-Branch-Net (only stage 4 fusion). The feature fusion depth of the multimodal collaborative network output is not enough, as it does not consider the multilevel information fusion of the network. However, Dual-Branch-Net fuses features at different levels. Thus, there is less information loss and more thorough fusion.  
Second, we compare our multimodal recognition method with a unimodal recognition method based on IKP or FV to study the necessity of recognition based on multimodal features. In the unimodal experiment, we first use Inception-ResNet-v1 to extract the features of the unimodal image and perform L2 regularization on output features. We then combine the triplet loss function to optimize the network. After that, the trained network model is used to extract the feature of the image pairs. Finally, according to  
Figure 6: ROC curves of ablation experiment results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **EER/\%** \\ \hline Inception-ResNet-v1 (IKP) & 2.373 \\ Inception-ResNet-v1 (FV) & 2.222 \\ Dual-Branch-Net (only stage 4 fusion) & 0.799 \\ Dual-Branch-Net & 0.422 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.  
the similarity between the feature vector of the test image and the target image, we can determine whether the experimenter corresponding to the test image is a real matcher or an impostor. From the experimental results in Figure 6 and Table 3, we find that the performance of multimodal recognition of FV and IKP is significantly better than that of the unimodal recognition of FV or IKP. Therefore, the result confirms the feasibility of the proposed multimodal recognition system.  
In addition, from the experimental results in Figure 6, the performance of FV recognition is better than that of IKP recognition. Therefore, we use FV as the primary task modality of Dual-Branch-Net, and IKP as the auxiliary task modality.