Contextual Patch Feature Learning for Face Recognition
Wenjing Liao · Journal of Software · 2014
Local features, such as local binary patterns(LBP), have shown better performance than global featurein the problem of face recognition. However, the methodsto extract the local features are usually given as fixed,and also neglect the class labels of the training samples.In this paper, we propose a novel algorithm to learn adiscriminate local feature from the small patches of theface image to boost the face recognition. The pixels of eachimage patch and its neighboring patches are both used toconstruct the local feature. The pixel vector of each patchis mapped to new subspaces by a transformation matrix,and mapped pixel vectors the neighboring patches are alsocombined to obtain the local feature vector. The subspacemapping parameter and the neighboring patch combinationparameter are learned to minimize the distances of localfeatures between the same person, and at the same timeto maximize that between different persons. We performexperiments on some benchmark face image database toshow the advantage of the proposed method.