NDLI: Learning ordinal discriminative features for age estimation

Content Provider	IEEE Xplore Digital Library
Author	Changsheng Li Qingshan Liu Jing Liu Hanqing Lu
Copyright Year	2012
Description	Author affiliation: CICE, Nanjing University of Information Science and Technology, Nanjing, China, 210044 (Qingshan Liu) \|\| NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 100190 (Changsheng Li; Jing Liu; Hanqing Lu)
Abstract	In this paper, we present a new method for facial age estimation based on ordinal discriminative feature learning. Considering the temporally ordinal and continuous characteristic of aging process, the proposed method not only aims at preserving the local manifold structure of facial images, but also it wants to keep the ordinal information among aging faces. Moreover, we try to remove redundant information from both the locality information and ordinal information as much as possible by minimizing nonlinear correlation and rank correlation. Finally, we formulate these two issues into a unified optimization problem of feature selection and present an efficient solution. The experiments are conducted on the public available Images of Groups dataset and the FG-NET dataset, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.
Starting Page	2570
Ending Page	2577
File Size	663325
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247975
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Aging Manifolds Correlation Training Optimization Feature extraction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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