# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 2 Related Works  
Handwriting identification is based on human handwriting to determine the identity of the writer [13]. Offline signature verification is more practical than online signature verification because it is more popular and its structural information is more intuitive to reflect the characteristics of the writer. The online signature verification mode is more robust than the offline signature verification mode because it captures the dynamic information of the signature in real time and is not easy for impersonators to copy [14]. There are three types of signature forgery: simple, random and skilled. In the case of simple forgery, the forger knows the name information of the signer, but does not know the real signature of the signer. In the case of random forgery, the forger knows the name of the signer or one of the real signatures. In the case of skilled forgery, both the signer's name information and real signature information are known to the forger and the forger often practices imitating the signature of the signer [15]. The features used in the identification method can be divided into global features, local features, statistical features and dynamic features. For Chinese offline handwriting, [PERSON] et al. [16] generated handwriting feature sets to identify handwriting samples by extracting curvature features of the stroke skeleton in four directions: horizontal, vertical, apostrophe and down. The samples were divided into reference handwriting and query handwriting. The similarity measurement method was used to find the writer of the corresponding handwriting. The handwriting of 10 people was randomly selected for the query and the number of characters per sample was 30, with an identification rate of 86%. [PERSON] et al. [17] proposed an offline signature identification method based on scale invariant feature transform (SIFT) for local details of signature images, which detects SIFT feature points of the signature image and extracts feature descriptors, performs matching according to the Euclidean distance, filters matching pairs through the ratio of adjacent distances and the angle difference of feature points and performs histogram statistics on the angle difference in the matched feature points to form an ODH feature vector. Finally, the identification is completed according to the number of matching pairs and the similarity of ODH feature vectors. 4 NsigComp2010 Database has fake signature, including real signature, fake signature and fake signature. The real signature is a signature written by the same author as the reference signature, the imitation signature is a signature written by other authors imitating the reference signature and the fake signature is a signature written by the same author as the reference signature but deliberately concealing the writing method. Tested on the local database, the error acceptance rate (EAR) was 5.3%, the error rejection rate (ERR) was 7%, the equal error rate (EER) was 6.7% and the EER was 20% on the 4 NsigComp2010 Database. GRAPHJ is a forensic tool for handwriting analysis that implements automatic detection of lines and words in handwritten documents. The main focus in feature extraction is to measure the number of parts, such as the distance between text and characters, as well as the height and width of characters. The relative position of the punctuation on the \"i\" character is also used as a parameter to infer the authorship [18; 19]. [PERSON] et al. [20] conducted a study on recognition of electronic signatures based on dynamic features, using writing duration, number of strokes and average writing strength per stroke as feature classification and collected three types of signature samples: simple, general and complex for classification. The results of discriminant analysis (DA), K-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) were all above 77% or more, which, to some extent, shows the feasibility of machine learning algorithms for classification of electronic signature handwriting recognition. [PERSON] et al.