基于纹理特征的离线手写图像性别分类

Ali Mirza, Momina Moetesum, I. Siddiqi, Chawki Djeddi
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引用次数: 29

摘要

从笔迹样本中预测个人的性别和其他人口统计属性提供了一个有趣的基础问题,以及应用研究问题。性别和笔迹的视觉外观之间的相关性已经被许多研究证实,目前的研究也是基于同样的想法。我们利用纹理测量作为区分男性和女性文字的属性。笔迹中的纹理信息是通过对笔迹图像应用一组Gabor滤波器来捕获的。将滤波器响应的均值和标准差值收集到矩阵中,并将矩阵的傅里叶变换作为特征。使用前馈神经网络进行分类。该技术在QUWI数据库的一个子集上进行了评估,在不同的实验设置下取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gender Classification from Offline Handwriting Images Using Textural Features
Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.
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