摘要
手写签名鉴别技术作为生物特征安全认证领域的重要技术之一,具有广泛的应用前景。为了提高手写签名鉴别的正确性,提出一种基于三层小波变换和CPN神经网络结合的方法。首先对手写签名样本图像采取滤波去噪、二值化、细化、归一化等预处理措施,然后使用离散DB3小波分解提取高通系数矩阵处理后作为样本特征进行提取,而后采用CPN神经网络分类器对4680个训练样本进行每样本7500次训练,最后使用训练完毕的分类器对待鉴别样本进行分类鉴别。在由36个鉴别实验组组成的实验数据集上,样本识别正确率达到了93.48%。通过多种方法的对比实验,结果表明该方法签名特征提取全面、分类识别效果明显优于线性分类器。
As one of the important technologies in the field of biometric authentication,handwritten signature authentication technology has a wide application prospect.In order to improve the accuracy of handwritten signature verification,a method combining wavelet transform and CPN neural network is proposed.First,we take some preprocessing measures such as filtering and denoising,binarization,thinning,and normalization to the signature sample image,then the text image is decomposed by DB3 wavelet and the decomposed high pass coefficient matrix is extracted and treated as the features,then the CPN neural network classifier is used to train 7500 times for each training sample.Finally,the trained classifier is used to classify and identify the samples.On an experimental data set consisting of 36 identification experiment groups,the sample recognition accuracy of the method reached 93.48%.Comparative tests of various methods were used,the results show that the signature feature extraction of this paper is comprehensive and the recognition effect is better than the linear classifiers.
作者
贾建忠
JIA Jian-zhong(School of Information and Engineering, Urumqi Vocational University, Urumqi 830001, China)
出处
《计算机与现代化》
2020年第7期27-31,共5页
Computer and Modernization
基金
教育部人文社会科学研究青年基金资助项目(15YJC880028)。
关键词
小波变换
特征提取
神经网络
签名鉴别
权向量
wavelet transform
feature extraction
neural network
signature verification
weight vector
