# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 4 Results
### Classification Results  
To verify the feasibility of this method, experiments were conducted on a local dataset. The extracted dynamic features are fused with static features to obtain the signature feature set, which consists of 12 features, including 6 dynamic features and 6 static features. The number of words contained in the signature is small and identification is difficult. The local dataset contains the signatures of 44 writers. The experiments were conducted with a sample of 15 writers' signatures for two major categories of simple forgery and skilled forgery and the samples were divided into training and test sets, according to 7:3, which were randomly selected in the original dataset. A variety of typical methods of machine learning was used for classification and, in addition, RNN and CNN were used for experiments with this feature, which showed that the method of fusing dynamic and static features was better in terms of classification accuracy. We found that any classifier does not work well when trained and tested on dynamic features or static feature sets alone. This is because we selected features that incorporate a representative part unique to dynamic and static, both of which complement each other, discarding relatively redundant parts and highlighting the uniqueness of dynamic and static features, while increasing interpretability and making it easier for the public to understand.  
For the multi-classification experiment of forged signatures, in order to verify the effectiveness of the selected features in the multi classification, the machine learning algorithm is used to conduct separate experiments for a different number of writers. The results are shown in Table 6. The effect is better when the number of writers is 5 or less and basically 100% can be classified correctly, the effect is above 90% when the number of writers is 10 and the effect is stable at about 90% when the number of writers is 15. Table 7 shows the experimental results of forged signature identification when the number of writers is 15. Overall, simple forged signatures are better than skilled forged signatures in terms of identification, which is consistent with the characteristics of forged signatures. Among simple forgeries, complex signatures are best identified with a DA classification accuracy of 100% and, among skilled forgeries, simple imitations are best identified with a DA classification accuracy of 93.3%.  
When deep learning is used for classification, CNN and LSTM network structures are mainly used and attention modules are added to the network. Table 8 shows the experimental results of forged signatures when the number of writers is 15. On the whole, the classification results of simple forgery are better than those of skilled forgery. The classification accuracy of complex signatures is better in simple forgery and the classification accuracy is 96.7%. The classification of complex forgery is better in skilled forgery, the average classification accuracy was 96.7% and the highest was 100%.  
Figure 18: Signature quadrilateral with different chain codes at the same angle. (**a**) Signature quadrilateral with chain code 7; (**b**) signature quadrilateral with chain code 0.