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.
@article{arxiv.1611.05755,
title = {Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs},
author = {Guilherme Folego and Marcus A. Angeloni and José Augusto Stuchi and Alan Godoy and Anderson Rocha},
journal= {arXiv preprint arXiv:1611.05755},
year = {2016}
}
Comments
XII WORKSHOP DE VIS\~AO COMPUTACIONAL (Campo Grande, Brazil). In XII Workshop de Vis\~ao Computacional (pp. 311-316) (2016)