# Handwritten Signature Verification Method Based on Improved Combined Features
## 2 Related Works
### Feature Extraction in Signatures  
The current research status of signature verification feature extraction algorithms mainly extracting signature texture features, geometric features, and dynamic features. [PERSON] et al. proposed an automatic recognition technology based on multi-level feature fusion and optimal feature selection [1], and calculated 22 Gray-Level Co-Occurrence Matrix (GLCM) features and 8 geometric features, geometric features were used to characterize the shape of the signature such as edge, area, etc., GLCM represented texture information such as each signature change, and then adopted a High-Priority Index Feature (HPFI) to combine these features and proposed a feature selection method based on Skewness-Kurtosis Controlled PCA (SKCPCA), from which the optimal feature was selected to divide the signature into forged and real signatures. The proposed system was verified on MCYT, GPDS and CEDAR data sets, and compared with existing methods, it had significantly enhanced the FAR and FRR, with FAR of 2.66%, 9.17% and 3.34%, the disadvantage was that in the feature selection process, the removed features may affect the system performance, and the removed features may had better results on other data sets. [PERSON] et al. proposed a signature verification method that relied on the author by using two different types of texture features, discrete wavelet features and Local Quantized Patterns (LQP) features [2], extracting two types of transformation based on the signature image. For each signature author, using One-class Support Vector Machines (OC-SVM) to establish two independent signature models, corresponding to LQP and wavelet features. For each signature author, obtaining two different verification scores, and integrate the scores of the two OC-SVMs based on the average method to obtain the final verification score. Through the test on the GPDS, MCYT, and CEDAR data sets, the Equal Error Rate (EER) was 12.06%, 11.46%, and 7.59% respectively, which verified the generality of the method. [PERSON] et al. extracted different features, and explained their impact on the recognition ability of the system. The calculated features include Histogram of Oriented Gradient (HOG) and geometric features such as length distribution, tilt distribution, and entropy. Based on the application of different machine learning classification techniques: Bagging Tree, Random Forest (RF) and SVM, the system was tested on the UTSig data set, and the experimental results showed that SVM was better than other classifiers, with an accuracy rate of 94% [3]. [PERSON] et al. used Local Ternary Patterns (LTP) and oriented Basic Image Features (oBIFs) texture descriptors to extract features [4], and projected the signature image into the feature space. When the signature to be tested was given, the authenticity was judged by combining the decision results of two SVM. The technology was tested on the Dutch and Chinese signature data sets of ICDAR 2011, and the accuracy rates were 97.74% and 75.98%, respectively. In order to solve the existing challenges in signature verification and improve the verification ability of the system, [PERSON] et al. proposed a new feature extraction method that uses a combination strategy to combine the Fisher vectors and KAZE features detected from the foreground and background images to form new features [5], after testing on the MCYT-75 data set, it was found that it had a lower error rate than the existing signature verification method. [PERSON] et al. proposed a handwritten signature verification method based on interval symbol representation and fuzzy similarity measure. In the feature extraction stage, local binary pattern (LBP) features are extracted from the signature image [6]. This method was tested on the GPDS dataset. When the number of training samples was 8 or more, the proposed method performs better in error rate. [PERSON] et al.