Name
Abstract | ||
|---|---|---|
Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of he... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIFS.2017.2686013 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Iris recognition,Probes,Markov random fields,Sensors,Robustness,Image sensors,Image resolution | Computer vision,Iris recognition,Image sensor,Pattern recognition,Computer science,Binary code,Robustness (computer science),Artificial intelligence,Pixel,Biometrics,Image resolution,Binary number | Journal |
Volume | Issue | ISSN |
12 | 10 | 1556-6013 |
Citations | PageRank | References |
7 | 0.46 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nianfeng Liu | 1 | 10 | 0.84 |
| Jing Liu | 2 | 7 | 0.46 |
| Zhenan Sun | 3 | 2379 | 139.49 |
| Tieniu Tan | 4 | 11681 | 744.35 |