Name
Abstract | ||
|---|---|---|
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.patcog.2014.05.020 | Pattern Recognition |
Keywords | Field | DocType |
Object recognition,Biometrics,Modeling and prediction,Statistical models | Recognition system,Pattern recognition,Computer science,Multinomial distribution,Fundamental pattern,Statistical model,Artificial intelligence,Biometrics,Machine learning,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
47 | 12 | 0031-3203 |
Citations | PageRank | References |
4 | 0.40 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suresh Kumar Ramachandran Nair | 1 | 4 | 0.40 |
| Bir Bhanu | 2 | 3356 | 380.19 |
| Subir Ghosh | 3 | 4 | 0.40 |
| Ninad Thakoor | 4 | 94 | 13.39 |