This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm
for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high …

In this paper, we raise important issues on scalability and the required degree of supervision
of existing Mahalanobis metric learning methods. Often rather tedious optimization …

Face identification is the problem of determining whether two face images depict the same
person or not. This is difficult due to variations in scale, pose, lighting, background …

In this paper, we first offer an overview of advances in the field of distance metric learning.
Then, we empirically compare selected methods using a common experimental protocol …

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 …

In this paper, we examine the generalization error of regularized distance metric learning.
We show that with appropriate constraints, the generalization error of regularized distance …

In this paper, we propose a new approach to improve face verification and person re-
identification in the RGB images by leveraging a set of RGB-D data, in which we have …

In this paper we present a generalization of classical multidimensional scaling called
DISTATIS which is a new method that can be used to compare algorithms when their outputs …

We present a method for training a similarity metric from data. The method can be used for
recognition or verification applications where the number of categories is very large and not …

This paper considers the person verification problem in modern surveillance and video
retrieval systems. The problem is to identify whether a pair of face or human body images is …