Face Recognition using Balanced Pairwise Classifier Training

Zhou, Ziheng, Chindaro, Samuel, Deravi, Farzin (2009) Face Recognition using Balanced Pairwise Classifier Training. In: International Conference on Information Security and Digital Forensics 2009, City University, London, 7-8 September 2009. . pp. 65-74. (doi:10.1007/978-3-642-11530-1_5) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:23512)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: http://dx.doi.org/10.1007/978-3-642-11530-1_5

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

Item Type:	Conference or workshop item (Paper)
DOI/Identification number:	10.1007/978-3-642-11530-1_5
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1653 Human face recognition
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	J. Harries
Date Deposited:	11 Jan 2010 10:14 UTC
Last Modified:	05 Nov 2024 10:03 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/23512 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhou, Ziheng.

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Chindaro, Samuel.

Creator's ORCID:
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Deravi, Farzin.

Creator's ORCID:	https://orcid.org/0000-0003-0885-437X
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