Name
Abstract | ||
|---|---|---|
In the use of hidden Markov model for face recognition, discrimination is weak, and the parameters of the model require a higher degree of precision. Therefore, this paper proposes a new method to obtain Fisher score features. This has fully considered the model parameters on the categories of the differential contribution, so precision of hidden Markov model parameters is lower. Because sole class discrimination is limited, this paper attempts to use multi-class Fisher score feature series in order to further improve the characteristics of the type of discrimination, the experiments proved that the Fisher score characteristics can greatly improve the face recognition rate. © 2012 IEEE. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FSKD.2012.6234309 | FSKD |
Keywords | Field | DocType |
kernel,hidden markov model,face,fisher kernel,feature extraction,vectors,hidden markov models,information matrix,face recognition | Kernel (linear algebra),Facial recognition system,Pattern recognition,Scoring algorithm,Computer science,Feature extraction,Artificial intelligence,Fisher information,Hidden Markov model,Fisher kernel,Class discrimination,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | null |
ISBN | Citations | PageRank |
978-1-4673-0025-4 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiying Xu | 1 | 0 | 0.34 |
| Jiao He | 2 | 0 | 0.34 |
| Chaocheng Xie | 3 | 0 | 0.34 |
| Xicheng Liu | 4 | 7 | 3.20 |