Cross-database face recognition and human age estimation
73 p.
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sg-ntu-dr.10356-580742023-07-04T15:39:45Z Cross-database face recognition and human age estimation Li, Mao Tan Yap Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics 73 p. Even though there are many conventional algorithms which can address issues such as face recognition and human age estimation, they can only achieve good results under the assumption that the training samples and testing samples follow similar probability distributions. For real-life applications, the difference between two distributions of the training samples and testing samples need to be taken into account. Recently, an effective subspace learning algorithm, which is developed using a Bregman divergence-based regularization, can figure out this difficulty efficiently by integrating a novel subspace learning framework of transfer learning into the conventional subspace learning algorithms. Master of Science (Signal Processing) 2014-04-07T12:13:01Z 2014-04-07T12:13:01Z 2011 2011 Thesis http://hdl.handle.net/10356/58074 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics Li, Mao Cross-database face recognition and human age estimation |
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73 p. |
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Tan Yap Peng |
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Tan Yap Peng Li, Mao |
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Theses and Dissertations |
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Li, Mao |
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Li, Mao |
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Cross-database face recognition and human age estimation |
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Cross-database face recognition and human age estimation |
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Cross-database face recognition and human age estimation |
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Cross-database face recognition and human age estimation |
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Cross-database face recognition and human age estimation |
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cross-database face recognition and human age estimation |
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2014 |
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http://hdl.handle.net/10356/58074 |
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