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.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Zhou, Z., Chindaro, S., Deravi, F. (2010). Face Recognition Using Balanced Pairwise Classifier Training. In: Weerasinghe, D. (eds) Information Security and Digital Forensics. ISDF 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11530-1_5
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DOI: https://doi.org/10.1007/978-3-642-11530-1_5
Publisher Name: Springer, Berlin, Heidelberg
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