# One-shot Representational Learning for Joint Biometric and Device Authentication

arXiv: https://arxiv.org/abs/2101.00524 · HTML: https://mlsanity.com/paper/2101.00524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Learning Deep Structure-Preserving Image-Text Embeddings](https://mlsanity.com/paper/1511.06078/) (2015)
- [Periocular Recognition Using CNN Features Off-the-Shelf](https://mlsanity.com/paper/1809.06157/) (2018)
- [Deep learning for source camera identification on mobile devices](https://mlsanity.com/paper/1710.01257/) (2017)
- [Smartphone Camera De-identification while Preserving Biometric Utility](https://mlsanity.com/paper/2009.08511/) (2020)

## Built on by (most cited first, 2 of 2)

- [One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations](https://mlsanity.com/paper/2307.05128/) (2023)
- [AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap](https://mlsanity.com/paper/2204.12492/) (2022)

## Abstract (arXiv)

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%.