Training of feature extractor via new cluster validity - Application to adaptive facial expression recognition

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 254
  • Download : 0
A lot of researches on classifiers, which can perform well with a given set of feature vectors, have been done. However, researches on feature vectors, which extract better feature vectors automatically, have not been done very much. We face two problems when we consider feature extraction process. One is how we can make a good feature extractor, and the other is what more separable features are. In this paper, we solved these two problems by proposing feature extractor-training methodology that uses new cluster validity as an objective function. By combining feature extractor to Fuzzy Neural Network Model, we achieve on-line adaptation capability as well as optimized feature extraction. The result shows recognition rate of 97% when on-line adaptation is being done.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2005-08
Language
English
Article Type
Article; Proceedings Paper
Citation

KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 4, PROCEEDINGS Book Series: LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, v.3684, pp.542 - 548

ISSN
0302-9743
URI
http://hdl.handle.net/10203/93179
Appears in Collection
BiS-Journal Papers(저널논문)EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0