## 4 Experiments
### Comparative Experiments  
To verify the effectiveness of the proposed method, we compare multiple methods, as shown in Table 2. We divide the methods in the table into the following four categories:  
1. Non-training-based unimodal methods: Including the wide line detector method [15], mean curvature method [16], and repeated line tracking method [17]. These three algorithms were common methods in FV recognition before deep neural networks became the mainstream method for feature extraction.
2. Non-training-based multimodal methods: Holistic [13] and nonlinear [13] are two methods using score-level fusion.
3. Training-based unimodal methods: Light CNN [41], ResNet-50 [32], and SqueezeNet [42] are all general networks based on CNN. [PERSON] et al. [43] used them to realize FV recognition by combining triplet loss.
4. Training-based multimodal methods: VGGNet-16 [31], Resnet-50, Resnet-101 [32], ResNet50-Softmax-Add [22], and AsymmetricNet [23]. The former three are also general convolutional networks. [PERSON] et al. [24] combined them with score fusion methods to complete identity recognition. The latter two, like our method, are based on feature-level fusion. We reproduced them for multimodal recognition based on IKP and FV.  
We found several phenomena from Table 2. First, the EERs of all multimodal methods are lower than those of the unimodal methods. The main reason behind this phenomenon is that multimodal recognition methods can use more biological information to distinguish individuals from more perspectives. Second, among multimodal recognition methods, most training-based methods can achieve better recognition results than non-training-based methods. This verifies the effectiveness of CNN in this field. Third, our proposed method achieves the lowest EER. Although AsymmetricNet and ResNet50-Softmax-Add both use feature-level fusion methods, their effects are not as good as our proposed Dual-Branch-Net. This is probably because the fusion methods they designed only fuse deep features of multiple modalities. In contrast, Dual-Branch-Net fuses multilevel multimodal features, making the fusion more thorough.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Non-Training-Based Method** & **EER/\%** & **Training-Based Method** & **EER/\%** \\ \hline \multirow{3}{*}{Unimodal} & Wide line detector [44] & 9.46 & SqueezeNet [43] & 3.7 \\  & Mean curvature [44] & 6.49 & Light CNN [43] & 10 \\  & Repeated line tracking [44] & 4.45 & ResNet-50 [43] & 5.6 \\ \hline \multirow{4}{*}{Multimodal} & Holistic [13] & 2.72 & VGGNet-16 * [24] & 2.4433 \\  & Nonlinear [13] & 2.45 & ResNet-50 * [24] & 1.0235 \\ \cline{1-1}  & & & ResNet-101 * [24] & 0.7859 \\ \cline{1-1}  & & & AsymmetricNet [23] & 3.393 \\ \cline{1-1}  & & & ResNet50-Softmax-Add [22] & 1.109 \\ \cline{1-1}  & & & Dual-Branch-Net (ours) & 0.422 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of comparative experiments. Non-training-based represents traditional methods without training, and training-based represents methods based on CNNs. The unimodal methods use only FV, while the multimodal methods use FV and IKP (methods with * use FV and hand shape).