Presentation 2006-06-16
Facial image classification by feature of generation using self-organization
Koichi Ikuta, Hiroshi Kage, Ken-ichi Tanaka, Kazuo Kyuma,
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Abstract(in English) As management of personal information becomes strict in recent years, it becomes difficult to acquire a large amount of facial image data with personal information like gender, generation, etc. For this reason, we consider applying self-organization map algorithm to the problem of generation classification. The algorithm is capable of learning the some feature topology from the feature space. The feature construction of generation has continuousness in the feature space of facial image. However, it is difficult to control formations of proper MAP in general. Therefore we combine supervised learning to the self-organization of generation feature so that supervised signals works as leads of construction of generation topology. We tested the algorithm by computer simulation.
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Keyword(in English) Self-organization MAP / facial image / generation classification / personal information
Paper # NC2006-31
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Committee NC
Conference Date 2006/6/9(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Facial image classification by feature of generation using self-organization
Sub Title (in English)
Keyword(1) Self-organization MAP
Keyword(2) facial image
Keyword(3) generation classification
Keyword(4) personal information
1st Author's Name Koichi Ikuta
1st Author's Affiliation Advanced Technology R&D Center Mitsubishi Electric Corporation()
2nd Author's Name Hiroshi Kage
2nd Author's Affiliation Advanced Technology R&D Center Mitsubishi Electric Corporation
3rd Author's Name Ken-ichi Tanaka
3rd Author's Affiliation Advanced Technology R&D Center Mitsubishi Electric Corporation
4th Author's Name Kazuo Kyuma
4th Author's Affiliation Advanced Technology R&D Center Mitsubishi Electric Corporation
Date 2006-06-16
Paper # NC2006-31
Volume (vol) vol.106
Number (no) 102
Page pp.pp.-
#Pages 4
Date of Issue