# Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs

arXiv: https://arxiv.org/abs/1611.05755 · HTML: https://mlsanity.com/paper/1611.05755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2016.

## Built on by (most cited first, 1 of 1)

- [On Brightness Agnostic Adversarial Examples Against Face Recognition Systems](https://mlsanity.com/paper/2109.14205/) (2021)

## Abstract (arXiv)

Cross-domain biometrics has been emerging as a new necessity, which poses several additional challenges, including harsh illumination changes, noise, pose variation, among others. In this paper, we explore approaches to cross-domain face verification, comparing self-portrait photographs ("selfies") to ID documents. We approach the problem with proper image photometric adjustment and data standardization techniques, along with deep learning methods to extract the most prominent features from the data, reducing the effects of domain shift in this problem. We validate the methods using a novel dataset comprising 50 individuals. The obtained results are promising and indicate that the adopted path is worth further investigation.