Continuous Authentication Through Finger Gesture Interaction for Smart Homes Using WiFi

Abstract

The development of smart homes has advanced the concept of user authentication to not only protecting user privacy but also facilitating personalized services to users. Along this direction, we propose to integrate user authentication with human-computer interactions between users and smart household appliances through widely-deployed WiFi infrastructures, which is non-intrusive and device-free. In this paper, we propose <inline-formula><tex-math notation= LaTeX >$FingerPass$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>F</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>P</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href= yu-ieq1-2994955.gif /></alternatives></inline-formula> which leverages channel state information (CSI) of surrounding WiFi signals to continuously authenticate users through finger gestures in smart homes. <inline-formula><tex-math notation= LaTeX >$FingerPass$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>F</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>P</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href= yu-ieq2-2994955.gif /></alternatives></inline-formula> separates the user authentication process into two stages, login and interaction, to achieve high authentication accuracy and low response latency simultaneously. In the login stage, we develop a deep learning-based approach to extract behavioral characteristics of finger gestures for highly accurate user identification. For the interaction stage, to provide continuous authentication in real time for satisfactory user experience, we design a verification mechanism with lightweight classifiers to continuously authenticate the user’s identity during each interaction of finger gestures. Experiments in real environments show that <inline-formula><tex-math notation= LaTeX >$FingerPass$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>F</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>P</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href= yu-ieq3-2994955.gif /></alternatives></inline-formula> can achieve the authentication accuracies of 90.6 percent under in-domain scenarios and 87.6 percent under cross-domain scenarios, as well as <inline-formula><tex-math notation= LaTeX >$186.6\\;ms$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>186</mml:mn><mml:mo>.</mml:mo><mml:mn>6</mml:mn><mml:mspace width= 0.166667em /><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href= yu-ieq4-2994955.gif /></alternatives></inline-formula> response time during interactions.