By the end of 2014, there were about 1.7 billion smartphone users worldwide. Mobile devices can carry large amounts of personal data but are often left unsecured. PIN locks are inconvenient to use and thus have seen low adoption (33% of users). While biometrics are beginning to be used for mobile device authentication, they are used only for initial unlock. Mobile devices secured with only login authentication are still vulnerable to theft in an unlocked state. This project studies a face-based continuous authentication system that operates in an unobtrusive manner. We present a methodology for fusing mobile device (unconstrained) face capture with gyroscope, accelerometer, and magnetometer data to correct for camera orientation and, by extension, the orientation of the face image. The project demonstrates (i) improvement of face recognition accuracy from face orientation correction, and (ii) efficacy of the prototype continuous authentication system. Additional face matching algorithms for matching unconstrained face images, and biometric traits such as motion, keystroke, and context will be studied.
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| Fig. 1 Sensors on a Nexus 5 Android phone provide a number of biometric traits such as image, motion, keystroke, and context. |
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| Fig. 2 A general framework for unobtrusive continuous authentication on a mobile device which is based on a biometric system. |
Relevant Publication(s)
1. D. Crouse, H. Han, D. Chandra, B. Barbello, and A. K. Jain, "Continuous Authentication of Mobile User: Fusion of Face Image and Inertial Measurement Unit Data", ICB, Phuket, Thailand, May 19-22, 2015. [pdf]
2. K. Niinuma, H. Han, and A. K. Jain, "Automatic Multi-view Face Recognition via 3D Model Based Pose Regularization", BTAS, Washington, D.C., Sept. 29-Oct. 2, 2013. [pdf]
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