# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 1 Introduction  
Biometric recognition is based on human characteristics and signatures are considered one of the most common biological features [1, 2]. The active mode of handwriting is widely associated with signature identification in biometric user authentication systems [3]. In a sense, handwriting is a behavioral manifestation of human thought, especially signatures, which have unique characteristics and strong personal style color [4]. Signature identification is required for office approval in corporate units, signing in cell phone business offices and banks, corroboration in the judicial industry and identification in examination scenarios. With the further development of information technology, the increasing popularity of handwritten signature acquisition devices and the maturity of digital writing technology have led to the replacement of pen-and-paper writing in the traditional sense. In the process of signature verification, dynamic features are the trend and static structure is the basis. If more comprehensive, simple and accurate methods appear, they will have a profound impact on various industries. However, as signature verification and identification systems are often used for forgery and fraud detection [5], the emergence of forged signatures complicates simple programs and even causes huge losses.  
Currently, there are two types of signature identification: offline and online. Offline handwriting identification materials use traditional writing tools to write handwriting information on paper, which is then captured as a picture by a camera or scanner [6]. The features extracted from offline images can be combined to form a variety of effective features with uniqueness that cannot be ignored. Online signatures are obtained by signing on touch screen devices, such as tablets and cell phones, and many features are obtained by using a special pen and tablet and a scanned signature image [7]. Online handwriting recognition can be performed by collecting rich information, such as writing speed, angle, strength used by writers and stroke order online [8]. The online data are very clear, captured on a digital device, consisting of a discrete number of samples [9] and contain some additional supporting information [10].  
With the popularity of paperless scenarios, online signature verification is widely used in various fields [11]. Electronic signatures are influenced by writing carriers and writing tools, resulting in many handwriting feature changes [12]. Handwriting is especially important from the perspective of handwriting verification but relying on handwriting signatures alone also loses some important features.  
This study uses a dot matrix pen tool to identify handwriting by combining the static features of traditional pen-and-paper writing with the dynamic features of digitized writing. The dot matrix digital pen is a writing tool that captures the pen's motion track of the pen through the high-speed camera at the front, obtains the pen tip pressure data by a pressure sensor and transmits the dynamic information of the writing process through coordination and pressure changes simultaneously. After preprocessing the written dynamic data and static image information, the easily understood structured static features and fine dynamic features are extracted and then the training and test sets are divided for each subtask and different models are used to study the fused features for classification and discrimination.  
The paper is organized as follows: Section 2 describes the work related to this study. Section 3 describes materials and methods. Section 4 shows the results of the study. Section 5 is the discussion. Section 6 provides a summary.