针对熟练伪造签名识别正确率较低的问题, 提出了基于笔划三维深度特征的个人签名识别方案。首先采集不同书写者的真实签名及他人的套摹签名, 用高精度的体式显微镜分别对签名笔划进行扫描, 获取签名笔划的表面三维点云数据。然后通过高斯滤波器滤掉三维点云数据中的噪声, 计算签名笔划的平均深度、沿笔划方向深度标准差以及熵等统计特征。之后, 对数据集进行数据增强, 增加签名数据的数量。最后, 将签名数据分为训练集和测试集, 在不同训练比例下使用分类器(包括SVM、KNN、ANN)进行分类。实验结果显示, 本文算法在本地签名数据集上的最佳识别正确率为9869%, 优于大多数传统算法, 满足实际应用的要求。

In order to solve the problem that the recognition rates of skilled forgery signatures is not satisfied, a personal signatures recognition scheme based on 3D depth feature of strokes is proposed. Firstly, the real signatures of different writers and the skilled forgery signatures are collected by high-precision stereo microscope, obtaining the surface 3D point cloud data of the signature strokes. Secondly, after filtering the noise of cloud data through the Gaussian filter, the statistical features such as the average depth of the stroke, the standard deviation of the depth along the stroke direction, and the entropy are calculated. To increase the number of data sets, data augmentation is implemented. Finally, the data set is divided into training set and test set, using classifier (including SVM, KNN, ANN) to classify the data set under different training ratios. The experimental results show that the best recognition accuracy of the algorithm on the local signature data set is 98.69%, which is better than most traditional algorithms and meets the requirements of practical applications.