Abstract

An efficient offline system for writer independent signature verification is proposed in this work which is quite a difficult task in computer vision. The proposed system employs a novel global representation of signatures followed by the Mahalanobis distance based dissimilarity score to discriminate between the original signatures and their skilled forgeries. The global representation of an image containing signature is based on aggregation of local descriptors using a vocabulary. The global descriptors from the pair of images are then used to learn a low-rank distance metric which is not a trivial task owing to the high dimensionality of the descriptor. The experimental results are reported on two datasets namely CEDAR and BHSig260; both containing a sufficient number of writers and are used as benchmark datasets. A comparison with the state-of-the-art approaches demonstrates the efficiency of our proposed approach. Our proposed method achieved 100% accuracy on CEDAR dataset and outperformed all other methods on BHSig260 (Bengali) dataset. The results on BHSig260 (Hindi) dataset are also promising.

中文翻译：

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一种离线学习者独立签名验证的度量学习方法

摘要

在这项工作中提出了一个有效的脱机系统，用于独立于作者的签名验证，这在计算机视觉中是一项艰巨的任务。所提出的系统使用签名的新颖的全局表示，然后使用基于马氏距离的不相似度评分来区分原始签名及其熟练的伪造品。包含签名的图像的全局表示基于使用词汇表的本地描述符的聚合。然后，使用这对图像中的全局描述符来学习低秩距离度量，由于该描述符的维数高，因此这不是一项琐碎的任务。实验结果报告在两个数据集上，分别是CEDAR和BHSig260。都包含足够数量的编写器，并用作基准数据集。与最新方法的比较证明了我们提出的方法的效率。我们提出的方法在CEDAR数据集上达到了100％的准确性，并且优于BHSig260（孟加拉）数据集上的所有其他方法。BHSig260（印地语）数据集上的结果也很有希望。