One-shot Representational Learning for Joint Biometric and Device Authentication

TL;DR

This paper introduces a one-shot learning method to simultaneously recognize individuals and devices from a single biometric image, enhancing security and privacy in mobile devices.

Contribution

It presents a novel joint representation learning approach for biometric and device recognition from a single image, evaluated across multiple biometric modalities.

Findings

Achieved up to 99.81% rank-1 accuracy

Attained 100% verification accuracy at 1% false match rate

Validated on iris, face, and periocular images

Abstract

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%.