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Kernel Learning Algorithms for Face Recognition pp 71–99Cite as

Kernel Principal Component Analysis (KPCA)-Based Face Recognition

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Abstract

As a sub-field of pattern recognition, face recognition (or face classification) has become a hot research point. In pattern recognition and in image processing, feature extraction based no dimensionality reduction plays the important role in the relative areas. Feature extraction simplifies the amount of resources required to describe a large set of data accurately for classification and clustering. On the algorithms, when the input data are too large to be processed and it is suspected to be notoriously redundant (much data, but not much information), then the input data will be transformed into a reduced representation set of features also named features vector with linear transformation or the nonlinear transformation. Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen, it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input data.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Yi-Kuang Street 2, Harbin, People’s Republic of China

    Jun-Bao Li

  2. School of Information and Engineering, Flinders University of South Australia, Sturt Road, Bedford Park, SA, 5042, Australia

    Shu-Chuan Chu

  3. HIT Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen City, Guangdong Province, People’s Republic of China

    Jeng-Shyang Pan

Authors
  1. Jun-Bao Li
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  2. Shu-Chuan Chu
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  3. Jeng-Shyang Pan
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Correspondence to Jun-Bao Li .

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© 2014 Springer Science+Business Media New York

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Li, JB., Chu, SC., Pan, JS. (2014). Kernel Principal Component Analysis (KPCA)-Based Face Recognition. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_4

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  • DOI: https://doi.org/10.1007/978-1-4614-0161-2_4

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-0160-5

  • Online ISBN: 978-1-4614-0161-2

  • eBook Packages: EngineeringEngineering (R0)

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