Face Recognition Using Balanced Pairwise Classifier Training

Ziheng Zhou; Samuel Chindaro; Farzin Deravi

Information Security and Digital Forensics. First International Conference, ISDF 2009, London, United Kingdom, September 7-9, 2009, Revised Selected Papers

Research Article

Face Recognition Using Balanced Pairwise Classifier Training

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@INPROCEEDINGS{10.1007/978-3-642-11530-1_5,
    author={Ziheng Zhou and Samuel Chindaro and Farzin Deravi},
    title={Face Recognition Using Balanced Pairwise Classifier Training},
    proceedings={Information Security and Digital Forensics. First International Conference, ISDF 2009, London, United Kingdom, September 7-9, 2009, Revised Selected Papers},
    proceedings_a={ISDF},
    year={2012},
    month={5},
    keywords={face recognition classification aging},
    doi={10.1007/978-3-642-11530-1_5}
}

Ziheng Zhou
Samuel Chindaro
Farzin Deravi
Year: 2012
Face Recognition Using Balanced Pairwise Classifier Training
ISDF
Springer
DOI: 10.1007/978-3-642-11530-1_5

Ziheng Zhou¹^,*, Samuel Chindaro¹^,*, Farzin Deravi¹^,*

1: University of Kent

*Contact email: z.zhou@kent.ac.uk, s.chindaro@kent.ac.uk, f.deravi@kent.ac.uk

Abstract

This paper presents a novel pairwise classification framework for face recognition (FR). In the framework, a two-class (intra- and inter-personal) classification problem is considered and features are extracted using pairs of images. This approach makes it possible to incorporate prior knowledge through the selection of training image pairs and facilitates the application of the framework to tackle application areas such as facial aging. The non-linear empirical kernel map is used to reduce the dimensionality and the imbalance in the training sample set tackled by a novel training strategy. Experiments have been conducted using the FERET face database.format.

Keywords: face recognition classification aging

Published: 2012-05-25

: http://dx.doi.org/10.1007/978-3-642-11530-1_5