Off-line Signature Verification Based on Multitask Learning

被引：0
|
作者：
Ji, You [1 ]
Sun, Shiliang [1 ]
Jin, Jian [1 ]
机构：
[1] E China Normal Univ, Dept Comp Sci & Technol, Shanghai 200241, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT III | 2011年 / 6677卷
关键词：
Off-line Signature Verification; Multitask Learning; Support Vector Machines; Machine Learning; MODIFIED DIRECTION FEATURE;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Off-line signature verification is very important to biometric authentication. This paper presents an effective strategy to perform off-line signature verification based on multitask support vector machines. This strategy can get a significant resolution of classification between skilled forgeries and genuine signatures. Firstly modified direction feature is extracted from signature's boundary. Secondly we use Principal Component Analysis to reduce dimensions. We add sonic helpful assistant tasks which are chosen from other tasks to each people's task. Then we use multitask support vector machines to build a useful model. The proposed model is evaluated on GPDS and MCYT data sets. Our experiments demonstrated the effectiveness of the proposed strategy.
引用
收藏
页码：323 / 330
页数：8
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