Off-feature information incorporated metric learning for face recognition

Abstract

Distance metric learning suppresses the intraclass variation while preserving the inter-class variation between two feature vectors. However, these two types of information are mixed in the feature vectors that need to be separated based on learning from the training data. The limited training data may not be able to well separate these two types of information and hence limits the effectiveness of metric learning. This letter proposes to exploit off-feature information to help suppress the intraclass variation of the feature vectors. For face recognition, some identity-independent information such as pose, expression, and occlusion is extracted from source images and utilized as the off-feature information to enhance the performance of distance metric learning. In training, the algorithm learns how to incorporate the off-feature information to suppress the intraclass variation of features. In recognition, the similarity score of a face image pair is determined by its distance in feature space and that of off-feature space. Extensive experiments demonstrate that the proposed off-feature information incorporated metric learning is helpful to suppress the intraclass variation of feature vectors, which visibly enhances the existing metric learning algorithms.