Face Recognition using Balanced Pairwise Classifier Training

Zhou, Ziheng and Chindaro, Samuel and Deravi, Farzin (2009) Face Recognition using Balanced Pairwise Classifier Training. In: International Conference on Information Security and Ditigal Forensics 2009, 7-8 September 2009, City University, London. (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)

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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)
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1650 Facial Recognition systems
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 11 Jan 2010 10:14
Last Modified: 12 Jun 2014 09:27
Resource URI: https://kar.kent.ac.uk/id/eprint/23512 (The current URI for this page, for reference purposes)
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