Name
Title | ||
|---|---|---|
Unifying Probabilistic Linear Discriminant Analysis Variants in Biometric Authentication. |
Abstract | ||
|---|---|---|
Probabilistic linear discriminant analysis (PLDA) is commonly used in biometric authentication. We review three PLDA variants - standard, simplified and two-covariance - and show how they are related. These clarifications are important because the variants were introduced in literature without argumenting their benefits. We analyse their predictive power, covariance structure and provide scalable algorithms for straightforward implementation of all the three variants. Experiments involve state-of-the-art speaker verification with i-vector features. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-44415-3_47 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
PLDA,speaker and face recognition,i-vectors | Probabilistic linear discriminant analysis,Speaker verification,Predictive power,Pattern recognition,Computer science,Artificial intelligence,Scalable algorithms,Biometrics,Machine learning,Covariance | Conference |
Volume | ISSN | Citations |
8621 | 0302-9743 | 14 |
PageRank | References | Authors |
0.78 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksandr Sizov | 1 | 96 | 4.54 |
| Kong-Aik Lee | 2 | 709 | 60.64 |
| Tomi Kinnunen | 3 | 1323 | 86.67 |