Name
Title | ||
|---|---|---|
View-Independent Face Recognition with Biological Features Based on Mixture of Experts |
Abstract | ||
|---|---|---|
The proposed view-independent face recognition model based on Mixture of Expert, ME, uses feature extraction, C1 Standard Model Feature, C1 SMF, motivated from biology on the CMU PIE dataset. The strength of the proposed model is using fewer training data as well as attaining high recognition rate since C1 Standard Model Feature and the combining method based on ME were jointly used. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.57 | ISDA |
Keywords | Field | DocType |
proposed view-independent face recognition,c1 smf,feature extraction,view-independent face recognition,biological features,fewer training data,cmu pie dataset,c1 standard model feature,high recognition rate,object recognition,face recognition,visualization,face,standard model,computational modeling | Training set,Facial recognition system,Pattern recognition,Computer science,Visualization,Feature extraction,Speech recognition,Feature (machine learning),Mixture of experts,Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference |
ISSN | Citations | PageRank |
2164-7143 | 3 | 0.47 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Hajiany | 1 | 5 | 1.20 |
| Nina Taheri Makhsoos | 2 | 5 | 1.20 |
| Reza Ebrahimpour | 3 | 314 | 24.55 |