BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks

• URL: http://arxiv.org/abs/2001.10223v1
• Date: Tue, 28 Jan 2020 09:25:06 GMT
• Title: BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural Networks
• Authors: Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Javier Ortega-Garcia
• Abstract summary: 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. We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs)
• Score: 6.5379404287240295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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