One-shot Representational Learning for Joint Biometric and Device Authentication
This work enhances security and privacy for smartphone users by jointly authenticating both the individual and the device from a single biometric input, addressing a need for more robust authentication schemes.
This paper proposes a one-shot method to simultaneously perform biometric recognition and device recognition from a single biometric image. The method achieved a rank-1 identification accuracy of up to 99.81% and a verification accuracy of up to 100% at a false match rate of 1% across 14,451 images from 15 sensors.
In this work, we propose a method to simultaneously perform (i) biometric recognition (i.e., identify the individual), and (ii) device recognition, (i.e., identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 15 sensors resulted in a rank-1 identification accuracy of upto 99.81% and a verification accuracy of upto 100% at a false match rate of 1%.