Name
Abstract | ||
|---|---|---|
Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. This can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach. |
Year | Venue | Field |
|---|---|---|
2017 | NCVPRIPG | Computer vision,Facial recognition system,Dictionary learning,Ranking,Pattern recognition,Image matching,Computer science,Popularity,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
24 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| sivaram prasad mudunuri | 1 | 23 | 3.06 |
| Shashanka Venkataramanan | 2 | 5 | 2.08 |
| Soma Biswas | 3 | 3 | 3.41 |