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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 5289-5297
Dihong Gong , Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Zhifeng Li , Shenzhen Key Lab of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
Dacheng Tao , Centre for Quantum Computation & Intelligent Systems, Faculty of Engineering and IT, University of Technology, Sydney, NSW 2007, Australia
Jianzhuang Liu , Dept. of Information Engineering, the Chinese University of Hong Kong, China
Xuelong Li , Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, China
ABSTRACT
In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition. First, a new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy. By densely sampling the encoded face image, sufficient discriminatory and expressive information can be extracted for further analysis. A new matching method is also developed, called identity factor analysis (IFA), to estimate the probability that two faces have the same underlying identity. The effectiveness of the framework is confirmed by extensive experimentation on two face aging datasets, MORPH (the largest public-domain face aging dataset) and FGNET. We also conduct experiments on the famous LFW dataset to demonstrate the excellent generalizability of our new approach.
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CITATION
Dihong Gong, Zhifeng Li, Dacheng Tao, Jianzhuang Liu, Xuelong Li, "A maximum entropy feature descriptor for age invariant face recognition", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 00, no. , pp. 5289-5297, 2015, doi:10.1109/CVPR.2015.7299166
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