English

One-shot Representational Learning for Joint Biometric and Device Authentication

Computer Vision and Pattern Recognition 2021-01-05 v1

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%.

Keywords

Cite

@article{arxiv.2101.00524,
  title  = {One-shot Representational Learning for Joint Biometric and Device Authentication},
  author = {Sudipta Banerjee and Arun Ross},
  journal= {arXiv preprint arXiv:2101.00524},
  year   = {2021}
}

Comments

Accepted in 25th International Conference on Pattern Recognition (ICPR), (Milan, Italy), January 2021

R2 v1 2026-06-23T21:42:50.611Z