The augmented usage of deep learning-based models for various AI related problems are as a result of modern architectures of deeper length and the availability of voluminous interpreted datasets. The models based on these architectures require huge training and storage cost, which makes them inefficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint devices. As a solution, in this work, our contribution is two-fold. 1) An efficient dimensionality reduction technique, to reduce the number of features to be considered and 2) a state-of-the-art model CNN-LSTM based hybrid architecture for online signature verification. Thorough experiments on the publicly available datasets MCYT, SUSIG, SVC confirms that the proposed model achieves better accuracy even with as low as one training sample. The proposed models yield state-of-the-art performance in various categories of all the three datasets.
翻译:扩大使用深层次学习模型解决与大赦国际有关的各种问题,是因为现代结构的长度更深,而且有大量可解释的数据集可用,基于这些结构的模型需要大量的培训和储存费用,使得这些模型无法有效地用于在线签名核查等关键应用程序,也无法有效地用于资源制约装置,作为解决办法,我们在这项工作中的贡献是双重的。 1) 高效的多元性减少技术,以减少需要考虑的特征数量,2) 以CNN-LSTM为基础的最先进的模式型CNN-LSTM混合结构,用于在线签名核查。关于公开提供的数据集MCYT、SUSIG、SVC的彻底实验证实,拟议的模型即使只有一个培训样本,也具有更高的准确性。