Large Scale Similarity Learning Using Similar Pairs for Person Verification

Yang Yang, Shengcai Liao, Zhen Lei, Stan Z. Li

Abstract:

In this paper, we propose a novel similarity measure and then
introduce an efficient strategy to learn it by using only similar
pairs for person verification. Unlike existing metric learning
methods, we consider both the difference and commonness of
an image pair to increase its discriminativeness. Under a pairconstrained Gaussian assumption, we show how to obtain the
Gaussian priors (i.e., corresponding covariance matrices) of
dissimilar pairs from those of similar pairs. The application
of a log likelihood ratio makes the learning process simple
and fast and thus scalable to large datasets. Additionally, our
method is able to handle heterogeneous data well. Results on
the challenging datasets of face verification (LFW and PubFig) and person re-identification (VIPeR) show that our algorithm outperforms the state-of-the-art methods.