L2-Norm metric learning applied to unconstrained face pair-matching

This paper proposes a metric learning algorithm based on the L2-Norm (L2ML) in the context of the face pair-matching problem as an attempt to overcome the low discriminatory power of most current descriptors when the operating conditions are unconstrained. The L2ML differs from other similar techniques by giving an efficient closed-form solution to a relatively simple optimization objective. As the experiments show, despite the simplicity, the performance of the proposed method is comparable to that of more complex state of the art techniques. In fact, the combination of only two descriptors in the L2ML space reaches an average accuracy of 84.97% in the challenging Image Restricted benchmark of the aligned Labeled Faces in the Wild (LFW) dataset.