Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs). This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB and e-BioDigitDB databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.

中文翻译：

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BioTouchPass2：使用时间对齐的递归神经网络的触摸屏密码生物识别

密码仍然每天用于所有类型的应用程序。但是，它们在许多情况下自身不够安全。这项工作通过两因素身份验证要求用户绘制密码的每个字符而不是照常键入密码，从而增强了密码方案。这项研究的主要贡献如下：i）我们介绍了新颖的MobileTouchDB公共数据库，该数据库是在无人监督的移动场景中获得的，不受位置，姿势和设备的限制。该数据库包含由217位用户执行的超过64K在线字符样本，具有94种不同的智能手机型号，以及多达6个获取会话。ii）我们对建议的方法进行了完整的分析，同时考虑了诸如动态时间规整（DTW）之类的传统身份验证系统和基于递归神经网络（RNN）的新颖方法。此外，我们提出了一种新颖的方法，称为时间对齐的递归神经网络（TA-RNNs）。这种方法结合了DTW和RNN的潜力，可以训练出更强大的系统来防御攻击。使用MobileTouchDB和e-BioDigitDB数据库对提议的方法进行了完整的分析。我们提出的TA-RNN系统优于现有技术，仅使用4位数字密码和每个字符一个训练样本即可达到最终2.38％的平均错误率。