# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 5 Discussion  
In this study, the method of combining dynamic and static feature extraction is used to achieve better results. For the whole signature, dynamic features pay more attention to fine and clear information and use the value of each sampling point to obtain other data, while offline images pay more attention to the overall and structural information and use static features to complete the macro supplement. From the two dimensions, we can integrate more comprehensive features and complement each other. In the aspect of dynamic features, the more prominent and special features in the writing process, such as writing speed and pressure, are selected. In the aspect of static features, the public's impression of the signature, such as the aspect ratio (the signature is flat or square) and the angle of the signature quadrangle (whether the signature is inclined to the left or right as a whole), is referred to. The features that can best represent the dynamic and static features are screened out, which can also achieve better results and be more easily recognized by the public. At present, there are few studies on dynamic and static signature identification and it is difficult to find a database for research. At the same time, we made a comparative analysis of the results of existing studies. [PERSON] et al. gradually improved the accuracy  
\begin{table} \begin{tabular}{c} **Complex** \\ **Imitation** \\ \end{tabular}  \\ \hline
2 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\
3 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 \\
4 & 100.0 & 100.  
when training with real samples of 3, 5, 8 and 10. The highest classification result was 97.83% when 10 samples were used [6]. In addition to fewer selected features, the others are basically consistent with the design of the dichotomous experiment in this study. When the number of writers was two, the accuracy of all tasks was 100%. When [PERSON] et al. studied the multi-classification recognition of electronic signatures, the multi-classification results of the machine learning algorithm for 3000 samples from 30 authors were more than 90% [20]. In this study, when the number of writers was 15, the machine learning algorithm was about 90%, but the effect was good and stable in the deep learning network. The selection of features by [PERSON] et al. is largely consistent with the features we used in the extraction of dynamic features [22] and, through correlation analysis, we know that the standard deviation and variance in velocity and other features have relatively low correlation. From the above discussion, we can see that this study is relatively comprehensive in feature extraction. When combining dynamic and static features, structural static features are added. When the amount of multi-classification identification is small, it can reach 100% and when the number of authors is 15, most of them can reach 96.7%. Under the condition of high accuracy, it is easier to understand, but the identification of individual tasks still needs to be improved. The scope of application of signature verification has spread throughout people's daily life and it is essential for the general public to identify reasonably and effectively. [PERSON] et al., for identity recognition, relied on human facial and body expressions from static and dynamic situations, incorporating different conditions. Experiments have shown that a fusion of static and dynamic features, which focus on different directions, works better and achieves perfect performance [27]. Such feature fusion is not only for identity recognition; for example, the line interruption caused by typhon can be predicted through the coordination of static and dynamic data [28] and multi-scale features and hierarchical features can be extracted for super-resolution image detection [29]. The effectiveness of feature fusion is fully demonstrated in various fields.