# Handwritten Signature Verification Method Based on Improved Combined Features
## 2 Related Works
### Classification in Signatures  
The current research status of handwritten signature verification algorithms and classifiers at home and abroad, mainly using neural networks, SVM and so on. [PERSON] et al. used the Convolutional Neutral Network (CNN) to directly learn the visual cues in the signature image, and the effect was significant on the GPDS dataset, with its EER reduced from 6.97% to 1.72% [12]. [PERSON] et al. suggested to solve this problem by modifyingthe network structure, using spatial pyramid pooling, and learning fixed-size features from variable signatures [13]. On the GPDS data set, better results were obtained. The experimental results showed that the use of more High-resolution signature images can improve performance. [PERSON] et al. used supervised CNN to learn to distinguish feature levels, which are divided into shallow features and deep features. In addition, they also proposed a location-related twin neural network to help learn more discriminative feature spaces. Good results had been achieved on various data sets [14]. [PERSON] et al. proposed a new CNN structure [15], named Large-Scale Signature Network (LS2 Net), which handled large-scale training sample problems through batch normalization. LS2 Net had achieved high accuracy on MCYT, CEDAR and GPDS datasets. Experiments showed that batch normalization had a significant contribution to performance. [PERSON] et al. applied Rank Support Vector Machine (RankSVM) for the first time to solve the signature verification task [16]. From the (Receiver Operating Characteristic (ROC) curve, it can be seen that the use of RankSVM maximizes the curve. The Area Under Curve (AUC) solved the imbalance problem to a certain extent. [PERSON] et al. proposed a method that combines a statistical signature verification model based on image edit distance and a deep neural network structure signature verification model. They believed that the structural feature model and the statistical feature model were completely different and have complementary advantages. On the MCYT and GPDS data sets, it was proved that combining structural models and statistical models can significantly improve performance and benefit from their complementary characteristics [17]. In view of the inability to obtain skilled forged signatures during the training process, [PERSON] et al. solved the problem by checking the different loss functions of CNN and met the substantive requirements for generalization of handwritten signature verification [18]. Through the analysis of the three loss functions of cross entropy, Cauchy-Schwarz divergence and hinge loss, they were combined into a dynamic multi-loss function, and proposed a new integration framework for using them in CNN at the same time. [PERSON] et al. proposed a Deep Multitask Metric Learning (DMML) classification method for handwritten signature verification. DMML mainly used the ideas of multi-task learning and transfer learning. Experiments had proved that DMML had better performance in verifying signature authenticity, skilled forgery and random forgery [19]. [PERSON] proposed the use of Local Stability-Weighted Dynamic Time Warping (LS-DTW) method to verify online signatures. Experiments on MCYT-100 and SVC2004 online signature data sets had achieved good results, effectively improving the speed and accuracy of online signatures system [20].  
A variety of signature features and classification methods are used in the literature, so this paper focuses on combining and comparing features and achieving complementarity between classifiers. In order to improve the handwritten signature verification method, we propose a score fusion method based on accuracy (SF-A) to achieve feature combination.