论文标题
使用复发性神经网络的生物识别验证
Biometric Signature Verification Using Recurrent Neural Networks
论文作者
论文摘要
基于复发性神经网络（RNN）的体系结构已成功应用于许多不同的任务，例如语音或手写识别，并具有最新的结果。这项工作的主要贡献是分析RNN在实际情况下在线签名验证的可行性。我们考虑了一个基于长期记忆（LSTM）的系统，其目标是从成对的签名中学习相似性指标。在实验工作中，考虑了由400个用户和4个分开的收购会话组成的生物保健数据库。我们提出的LSTM RNN系统的表现优于最新发表的关于生物保健基准测试的作品的结果，该数字在17.76％至28.00％的相对验证性能改善方面的相对验证性能改善不等。
Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-the-art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeries.
