Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Face verification,Computer vision,Pattern recognition,Computer science,Portrait,Artificial intelligence,Deep learning,Biometrics,Standardization |
DocType | Volume | Citations |
Journal | abs/1611.05755 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guilherme Folego | 1 | 0 | 0.68 |
| Marcus A. Angeloni | 2 | 47 | 3.59 |
| jose augusto stuchi | 3 | 3 | 1.06 |
| Alan Godoy | 4 | 1 | 1.38 |
| Anderson Rocha | 5 | 913 | 69.11 |