Face Recognition Using Balanced Pairwise Classifier Training

This paper presents a novel pairwise classification framework for face recognition (FR). In the framework, a two-class (intraand 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.