Writer verification using texture-based features
Writer verification using texture-based features
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DOI：
10.1007/s10032-011-0166-4
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发表时间：
2012-09-01
影响因子：
2.3
通讯作者：
Sabourin, R.
中科院分区：
文献类型：
--
作者：
Hanusiak, R. K.;Oliveira, L. S.;Sabourin, R.
在这项工作中，我们提出了一个作者验证系统，它考虑了基于纹理的特征和不同的表示。笔迹的纹理是基于书写者的固有属性创建的。该方法独立于书写风格，减少了行、词和字符之间的间距，产生了保持主要特征的纹理，从而避免了分割的复杂性。我们还讨论了核查制度的一个重要问题，即用于培训的作者人数。我们的实验表明，写入者的数量对总体错误率没有重要影响，但对减少验证系统的错误接受有重要作用。我们发现，错误接受程度随着作者数量的增加而减少。最后，利用最大似然分析对不同纹理描述子训练的不同分类器生成的ROC曲线进行组合，得到ROC组合分类器。在一个由315名作者组成的数据库上进行的一组实验表明，基于纹理的特征和ROC组合方案是有效的。实验结果表明，总体误码率约为4%。这场演出堪比最先进的表演。此外，该组合方案能够在保持相同的真阳性率的情况下显著降低假阳性率。
In this work, we propose a writer verification system that takes into account texture-based features and dissimilarity representation. Textures of the handwritings are created based on the inherent properties of the writer. Independent of the writing style, the proposed method reduces the spaces between lines, words, and characters, producing a texture that keeps the main features thus avoiding the complexity of segmentation. We also address an important issue of verification system, i.e., the number of writers used for training. Our experiments show that the number of writers do not have an important impact on the overall error rate, but it has an important role in reducing the false acceptance of the verification system. We show that the false acceptance decreases as the number of writers increases. Finally, the ROC curves produced by different classifiers trained with different texture descriptors are combined using the maximum likelihood analysis, producing a ROC combined classifier. A set of experiments on a database composed of 315 writers show the efficiency of the texture-based features and the ROC combination scheme. Experimental results report an overall error rate of about 4%. This performance compares to the state of the art. Besides, the combination scheme is able to considerably reduce the false-positive rates while maintaining the same true-positive rates.