CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor刘成林
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword脱机手写签名验证 Siamese网络 特征提取 度量学习
Other Abstract

(1)对传统的基于特征提取和度量学习的签名验证方法进行了实验评价。本文详细介绍了轮廓波变换(Contourlet transform),Local Binary Pattern(LBP)特征,以及Histogram of Orientation Gradient(HOG)特征,并用支持向量机做书写者无关度量,在CEDAR和GPDSsynthetic这两个数据集进行实验,在大型数据集GPDSsynthetic上的结果可以作为传统特征的benchmark。



Handwritten signatures have been used popularly in daily life as an important means of identity authentication, for various types of documents, contracts, and bank checks. There are two main approaches for signature verification: writer-dependent and writer-independent. Considering the requirements of practical applications, we study into methods for writer-independent offline handwritten signature verification. We present an offline signature verification method using Siamese convolution neural network. Unlike the existing methods which design feature extraction and metric learning in two independent stages, we adopt a deep-leaning based framework which combines the two stages together and can be trained end-to-end. Our experimental results on two public datasets (GPDSsynthetic and CEDAR) demonstrate the superiority of our method on the offline handwritten signature verification problem. The main work and results of this thesis are as follows:
We evaluate some representative traditional signature verification methods based on feature extraction and metric learning. This thesis introduces the Contourlet transform, Local Binary Pattern(LBP) features, and Histogram of Orientation Gradient(HOG) features in detail, and uses the support vector machine as a writer-independent metric method to perform experiments on two public datasets, CEDAR and GPDSsynthetic. The results on the largest dataset GPDSsynthetic can be used as benchmarks for traditional features.
Based on the Siamese network , we combine feature extraction and metric learning in a unified framework. We design neural network architecture based on some classic network structures. Our method achieve good performance on the GPDSsynthetic and CEDAR datasets.

Document Type学位论文
Recommended Citation
GB/T 7714
幸子健. 脱机手写签名验证方法研究[D]. 北京. 中国科学院研究生院,2018.
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