Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89373
Title: Localized multifeature metric learning for image-set-based face recognition
Authors: Lu, Jiwen
Wang, Gang
Moulin, Pierre
Keywords: Face Recognition
Image Set Classification
Issue Date: 2015
Source: Lu, J., Wang, G., & Moulin, P. (2016). Localized multifeature metric learning for image-set-based face recognition. IEEE Transactions on Circuits and Systems for Video Technology, 26(3), 529-540.
Series/Report no.: IEEE Transactions on Circuits and Systems for Video Technology
Abstract: This paper presents a new approach to image-set-based face recognition, where each training and testing example is a set of face images captured from varying poses, illuminations, expressions, and resolutions. While a number of image set based face recognition methods have been proposed in recent years, most of them model each face image set as a single linear subspace or as the union of linear subspaces, which may lose some discriminative information for face image set representation. To address this shortcoming, we propose exploiting statistics information as feature representations for face image sets and develop a localized multikernel metric learning algorithm to effectively combine different statistics for recognition. Moreover, we propose a localized multikernel multimetric learning method to jointly learn multiple feature-specific distance metrics in the kernel spaces, one for each statistic feature, to better exploit complementary information for recognition. Our methods achieve state-of-the-art performance on four widely used video face datasets including the Honda, MoBo, YouTube Celebrities, and YouTube Face datasets.
URI: https://hdl.handle.net/10356/89373
http://hdl.handle.net/10220/44902
ISSN: 1051-8215
DOI: http://dx.doi.org/10.1109/TCSVT.2015.2412831
Rights: © 2015 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.