# Handwritten Signature Verification Method Based on Improved Combined Features
## 1 Introduction  
Handwritten signatures are widely used in life. With the development of machine learning and artificial intelligence, the research on handwritten signature verification is also deepening.  
In terms of signature data collection methods, there are mainly two kinds: offline signature image and online signature data. Offline signature image refers to the handwritten name of the author on the paper, which is then transmitted to the computer through the scanning device to form the signature image, and then verified according to the image features. Online signature refers to verification based on signature tracks, such as coordinates and pressures during writing. In this paper, an intelligent pen with pressure sensor and camera is used to write the name on the paper with full dots. In the process of writing the signature, the offline image and online data of the signature are collected in real time.  
Signature verification usually includes two stages of training and testing. In the training stage, different numbers of real signatures are used for preprocessing and featureextraction and then put into the classifier to obtain the model. In the test stage, the signatures are put into the classifier for comparison and output verification result.  
In the feature extraction stage, offline signature image features are called static features, which are mainly divided into local features and global features. Local features are mainly divided into texture features and gradient features, and global features are mainly geometric features. Online signature data features are called dynamic features, which are mainly divided into parameter-based features and function-based features. Parameters-based mainly refer to the signature duration and the number of pen tip upwards. Function-based features mainly refer to signature trajectories and pressure data. Dynamic features based on functional features generally have better results.  
There are two main verification methods, model-based verification and distance-based verification. Model-based methods mainly describe data distribution by generating models such as hidden Markov model (HMM), CNN, and SVM. The distance-based approach mainly uses the distance measures to compare the test signature with the reference signature through DTW. This paper uses SVM to process offline signature images and DTW to process online signature data.  
There are two main difficulties in signature verification. One is that there is a large intra-class and inter-class variability. The author's real signature will also change with time, age and other factors, and the forger will also imitate the signature with a lot of training in advance, so it is necessary to extract and select more comprehensive and representative signature features. Second, in real life scenarios, only a small amount of real signatures can be obtained for training, and insufficient data is also a problem that needs to be solved.  
In order to solve these challenges, this paper proposes a score fusion method based on accuracy weighting, which combines the static features of offline signature images and the dynamic features of online signature data through score fusion. Specifically, the image and data are preprocessed and feature extracted respectively, and then the images and data are verified by SVM and DTW respectively. Through the two classifiers, we can get the verification results and decision scores of offline signature and online signature. Due to the different verification results between classifiers, there is a certain degree of complementarity. Finally, we use score fusion to achieve offline and online combination and solve the problem of complementarity between classifiers.  
The remainder of the paper is organized as follows: Section 2 introduces related work of this study. Section 3 gives a detailed introduction to the proposed method. Section 4 is the experimental results and discussion. Section 5 presents the conclusion.