Name
Abstract | ||
|---|---|---|
ABSTRACT The embedded,Bayesian networks,(EBN) introduced in this pa- per, are a generalization of the embedded hidden Markov mod- els previously used for face and character recognition. An EBN is defined recursively as a hierarchical structure where,the ”par- ent” node is a Bayesian network,(BN) that conditions the EBNs or the observation sequence that describes the nodes of the ”child” layer. With an EBN, one can model complex N-dimensional data, avoiding the complexity of N-dimensional BN while still preserv- ing their flexibility and partial scale invariance. In this paper we present an application of the EBNs for face recognition and show the improvement,of this approach,versus the ”eigenface” and the embedded,HMM approaches. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICME.2002.1035530 | Multimedia and Expo, 2002. ICME '02. Proceedings. 2002 IEEE International Conference |
Keywords | Field | DocType |
belief networks,face recognition,hidden Markov models,EBN,character recognition,child layer,complex N-dimensional data,embedded Bayesian networks,embedded hidden Markov models,face recognition,hierarchical structure,parent node | Facial recognition system,Eigenface,Scale invariance,Pattern recognition,Computer science,Speech recognition,Bayesian network,Artificial intelligence,Hidden Markov model,Recursion,Principal component analysis,Bayesian probability | Conference |
Volume | Citations | PageRank |
2 | 42 | 1.94 |
References | Authors | |
11 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ara V. Nefian | 1 | 73 | 4.31 |