Writer's Gender Classification Using HOG and LBP Features

被引：5

作者：

Bouadjenek, Nesrine ^{[1
]}

Nemmour, Hassiba ^{[1
]}

Chibani, Youcef ^{[1
]}

机构：

[1] USTHB, LISIC Lab, Fac Elect & Comp Sci, Algiers, Algeria

来源：

RECENT ADVANCES IN ELECTRICAL ENGINEERING AND CONTROL APPLICATIONS | 2017年 / 411卷

关键词：

Gender classification; HOG; LBP; SVM;

D O I：

10.1007/978-3-319-48929-2_24

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The gender identification in handwritten documents becomes to gain importance for various writer authentication purposes. It provides information for anonymous documents for which we need to know if they were written by a Man or a Woman. In this work, we propose a system for writer's gender classification that is based on local textural and gradient features. Especially our proposed features are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), which are successful in various pattern recognition applications. The classification step is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset showed that the proposed features provide quite promising results.

引用

页码：317 / 325

页数：9

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