A multi-feature selection approach for gender identification of handwriting based on kernel mutual information
A multi-feature selection approach for gender identification of handwriting based on kernel mutual information
复制标题
基于核互信息的笔迹性别识别多特征选择方法
DOI：
10.1016/j.patrec.2018.05.005
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发表时间：
2019-04-15
影响因子：
5.1
通讯作者：
Tan, Jun
中科院分区：
文献类型：
--
作者：
Bi, Ning;Suen, Ching Y.;Tan, Jun
本文提出了一种新的灵活的方法来预测的笔迹样本的作者的性别。笔迹特征，如倾斜，曲率，行分离，链码，字符形状等，可以从不同的方法中提取。因此，多特征集是不相关的和冗余的。集合中存在特征冲突，影响分类的准确性和计算成本。本文提出了一种方法，命名为核互信息（KMI），侧重于特征选择。KMI方法可以减少冗余和冲突。此外，它提取了一个最佳子集的功能，从男性和女性作家的写作样本。为了保证KMI能够应用各种功能，本文介绍了手写体分割和手写体文本识别所采用的技术。分类进行了使用支持向量机（SVM）的两个数据库。第一个数据库来自ICDAR 2013年性别预测竞赛，该竞赛提供了阿拉伯语和英语样本。另一个数据库包含中文注册文档表格（RDF）数据库。在两个数据库上对所提出的方法和比较的方法进行了评价。从方法的结果突出了特征选择的重要性，从笔迹的性别预测。(c)2018爱思唯尔B.V.保留所有权利。
This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features like slant, curvature, line separation, chain code, character shapes, and more, can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes an approach, named kernel mutual information (KMI), that focuses on feature selection. The KMI approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. To ensure that KMI can apply the various features, this paper describes the handwriting segmentation and handwritten text recognition technology used. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, which provides the samples in both Arabic and English. The other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting. (c) 2018 Elsevier B.V. All rights reserved.