Biometric verification systems are used to recognize people based on their uniqueness or characteristics. Signature is considered as one of the most commonly used biometric that individualizes a human being. It is generally used to keep individual’s privacy in many places such as banking sectors, academic institutes, office premises and trading. But increase of criminal attempts in falsifying an individual’s signature, known as signature forgeries, motivates the researchers to develop computerized systems that can verify the genuineness of a questioned signature. Though many researches have been performed till date, but the issue of identifying skilled forgeries still remains a major concern for the researchers. To this end, in this work, we have designed a two-tier ensemble based writer dependent and language- invariant online signature verification system. In doing so, we have first extracted three different categories of features from each input signature: physical, frequency based and statistical, and then designed a feature-classifier based ensemble (i.e., Ensemble#1) using seven different classifiers. The predictions obtained from the seven classifiers are combined using normalised distribution summation strategy. Decisions obtained from Ensemble#1 are then fed to Ensemble#2, where a majority voting based approach is followed, to identify the input signature as genuine or forged. Our system is evaluated on two standard datasets: SVC 2004 (Task-II) and MCYT-100 in a writer dependent way. The equal error rate (ERR) and accuracy on SVC 2004 dataset are 2.20 and 98.43% respectively, and on MCYT-100 dataset these are 2.84 and 97.87% respectively. The GAR@0.01FAR value obtained for the SVC-2004 dataset is 94.50% while it is 92.90% for MCYT-100 dataset. We have also compared our results with some state-of-the-art methods, and it has been found that our method performs better than most of these methods. The code of this work is available at: https://github.com/prat1999/Online_Signature_Verification.

生物特征验证系统用于根据人们的独特性或特征来对其进行识别。签名被认为是使人类个体化的最常用的生物特征之一。它通常用于在银行，学术机构，办公场所和交易等许多地方保护个人隐私。但是，伪造个人签名的犯罪尝试的增加（称为签名伪造）促使研究人员开发可以验证所质疑签名真实性的计算机系统。尽管迄今为止已经进行了许多研究，但是识别熟练伪造品的问题仍然是研究人员关注的主要问题。为此，在这项工作中，我们设计了一个基于两层集成的基于作者的和语言不变的在线签名验证系统。为此，我们首先从每个输入签名中提取了三种不同的特征类别：物理特征，基于频率特征和统计特征，然后使用七个不同的分类器设计了基于特征分类器的集成（即Ensemble＃1）。使用归一化分布求和策略将从七个分类器获得的预测进行组合。然后将从Ensemble＃1获得的决策馈送到Ensemble＃2，在该过程中，将采用基于多数投票的方法，以将输入签名识别为真实签名或伪造签名。我们的系统在两个标准数据集上进行了评估：SVC 2004（任务II）和MCYT-100（与编写者相关）。SVC 2004数据集的相等错误率（ERR）和准确性为2.20和98。分别为43％和MCYT-100数据集上的分别为2.84和97.87％。对于SVC-2004数据集获得的GAR@0.01FAR值为94.50％，而对于MCYT-100数据集则为92.90％。我们还将我们的结果与一些最新方法进行了比较，发现我们的方法比大多数这些方法的性能更好。这项工作的代码可在以下网址获得：https：//github.com/prat1999/Online_Signature_Verification。