Presentation 2006/9/23
Face recognition using feature fusion and information criterion : K-nearest neighbor learning based on Mahalanobis distance
Takami SATONAKA, Keiichi UCHIMURA,
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Abstract(in English) We describe the adaptive metric learning procedure combining the asymptotic variance estimation and fine adjustment of the metric parameters for the face recognition. The metric learning model based on the Maha-lanobis distance suffered from the degraded performance due to the limitation of available training samples. For accurate estimation of metric parameters, the asymptotic model sharing common variance among classes with difference averages has been introduced. The MDL criterion is formulated to decide the optimal number of classes in the asymptotic statistical model. The feature fusion method assuming the synthesized feature patterns is proposed to construct the Kth-nearest neighbor learning network employing multiple Mahalanobis decision function and improves the performance with the increase of the metric function per class. We compare the simulation results using the ORL databases with these of other systems. The hybrid of the asymptotic variance estimation and fine adjustment of the metric parameters has attained the minimal recognition error rate.
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Keyword(in English) face recognition / Mahalanobis distance / Information criterion / MDL
Paper # HCS2006-48
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Committee HCS
Conference Date 2006/9/23(1days)
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Registration To Human Communication Science (HCS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Face recognition using feature fusion and information criterion : K-nearest neighbor learning based on Mahalanobis distance
Sub Title (in English)
Keyword(1) face recognition
Keyword(2) Mahalanobis distance
Keyword(3) Information criterion
Keyword(4) MDL
1st Author's Name Takami SATONAKA
1st Author's Affiliation Kumamoto Prefectual College of Technology()
2nd Author's Name Keiichi UCHIMURA
2nd Author's Affiliation Faculty of Engineering, Kumamoto University
Date 2006/9/23
Paper # HCS2006-48
Volume (vol) vol.106
Number (no) 268
Page pp.pp.-
#Pages 6
Date of Issue