Off-Line Signature Verification Based on Multi-Scale Local Structural Pattern

被引：6

作者：

Wen, Jing ^{[1
,2
]}

Fang, Bin ^{[1
]}

Zhang, Liang ^{[1
]}

Zhu, Yu ^{[1
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China

[2] Chengdu Univ, Key Lab Pattern Recognit & Intelligent Informat P, Inst Higher Educ Sichuan Prov, Chengdu, Peoples R China

来源：

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2017年 / 31卷 / 06期

关键词：

Off-line signature verification; local structural pattern; encoding feature; probability distribution function; Chi-square distance; pattern recognition; FISHER DISCRIMINANT METHOD; WRITER IDENTIFICATION; FACE RECOGNITION; INFORMATION; FEATURES; WAVELET; STATE; ART;

D O I：

10.1142/S0218001417560109

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper proposes a theoretically very simple, and effective multi-scale local structural patterns (MS-LSP) for off-line signature verification. The proposed strategy models the signature by probabilistically counting the distribution of each local structural pattern on multiple scales. This representation correlates with the writing style of an individual and depict the shape and structural information of signatures. Extensive experiments on two challenging databases (MCYT, GPDS960) show that the proposed approach achieves noticeably verification rates and it is expected to provide a powerful discriminative representation of the signature.

引用

页数：16

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