## 5 Experiments
### Comparison with the Classical Method  
The method in this paper is compared with the existing state-of-the-art methods on indicators such as FAR, FRR, and ACC on different datasets. Among them, the dataset contains CEDAR, BHisig260, and the Chinese dataset, and there is no order restriction between signature images in the experimental setting. Through such an experimental design, we can more comprehensively evaluate the advantages of each method. The CEDAR signature dataset [6] contains signature samples of English names. It is composed of 55 individuals' samples, and each individual has 24 genuine and 24 forged signatures. We compare other methods: Grid Feature [31], Surroundedness Feature [32], CBCapsNet [23], DeepHSV [33], HOCCNN [16] and SigNet-F [34]. Table 5 shows the results of the various methods, with CBCapsNet and DeepHSV achieving the best accuracy performance of 100% for the validation of the CEDAR dataset. After an experimental evaluation of this dataset, the fact that our FC-ResNet performs higher in terms of all of the reported evaluation metrics confirms the superior performance of our FC-ResNet. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **FAR (\%)** & **FRR (\%)** & **ACC (\%)** \\ \hline Chinese & 3.44 & 3.13 & 96.72 \\ Uyghur & 4.17 & 3.02 & 96.41 \\ Kazakh & 4.48 & 3.23 & 96.15 \\ Kirgiz & 3.48 & 3.18 & 96.67 \\ \hline \end{tabular}
\end{table} 
Table 4: Experimental results of FC-ResNet on self-built datasets. Figure 7: Accuracy rate (%) of our proposed method at different batch sizes. The BHSig260 dataset [8] contains two subsets: the BHSig-B Dataset and BHSig-H Dataset. The BHSig-B Dataset contains signature samples of Bengali names. It contains 100 individuals' signature samples. Each individual has 24 genuine signatures and 30 forged signatures. We compare other methods, including IDN [17], CBCapsNet [23] and SURDS [24]. Tables 6 and 7 show the results of different methods on the two datasets, respectively, with the CBCapsNet [23] method on the BHSig-H dataset showing the highest accuracy of 100%. On these two datasets, our proposed method has obvious advantages over other methods. In particular, our proposed method has low FAR and FRR and high accuracy, and it performs well on the signature verification problem on three public datasets, verifying its effectiveness and generalization ability. In addition, our model has fewer parameters than other models, has the advantage of being lightweight, and is suitable for resource-constrained practical application scenarios. Our Chinese signature dataset includes the signature samples of 200 individuals. We compared other methods, including IDN [17], STN [35], DenseNet-36 [20], IDN+SE+ESA [38], etc. Table 8 shows the results of various methods. According to the experimental results, our FC-ResNet model shows better results for all the evaluation metrics, indicating that our proposed approach has a higher generality and robustness. In summary, our proposed method shows excellent experimental results for different datasets, which fully proves its effectiveness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Method** & **FAR (\%)** & **FRR (\%)** & **ACC (\%)** \\ \hline [PERSON] et al. [33] & DeepHSV & - & - & 88.08 \\ [PERSON] et al. [17] & IDN & 4.12 & 5.24 & 95.32 \\ [PERSON] et al. [35] & STN & 3.96 & 3.96 & 96.04 \\ [PERSON] et al. [23] & CBCapsNet & 5.11 & 6.29 & 94.3 \\ [PERSON] et al. [36] & AVN & 5.07 & 7.33 & 93.8 \\ [PERSON] et al. [37] & Geometrical features & - & - & 97.79 \\ [PERSON] et al. [24] & SURDS & 19.89 & 5.42 & 87.34 \\ [PERSON] et al. [38] & IDN + SE + ESA & 1.5 & 3.14 & 97.17 \\ [PERSON] et al. [39] & MA-SCN & 9.96 & 5.85 & 92.86 \\ [PERSON] et al. [40] & CNN & - & - & 95 \\ Ours & FC-ResNet & 0.73 & 2.42 & 98.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison on the BHSig-B dataset.