用于在线签名认证的特征提取和个性化特征选择方法

提出一种在线签名认证中的特征提取和特征选择的方法.采用一种F-Tablet手写板采集签名数据.该手写板的特点是不仅可记录签名时的字形信息(x，y)序列，还可记录签名时的五维力信息(F_x，F_y，F_z，M_x，M_y)序列.从每个签名中提取3个等级共188个特征，接着定义特征重要性函数F，然后根据特征的重要性函数F的值对选取的188个特征进行排序，对F设不同的阈值就可完成不同的特征选择.在认证过程中使用SVM算法对选取的特征进行训练，然后用训练所得的模型进行验证.该方法的错误拒绝率为1.2%，错误接受率为3.7%.

Feature Extraction and Personalized Feature Selection for Online Signature Verification

An online signature verification algorithm is presented based on feature extraction and feature selection. A novel digital tablet, called F-Tablet, is used to capture the signature information. The tablet can capture both shape series and five-dimensional forces. Total 188 features are extracted from each signature and then divided into three classes. Then, the weight function of features F is defined and the 188 features are sorted according to the F values. With different thresholds, different feature sets are obtained. The SVM is used to train the selected feature sets in the training process and the signatures are verified by the trained models. The proposed algorithm achieves false rejection rate (FRR) of 1.2% and false acceptance rate (FAR) of 3.7%.