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 …

We consider the problem of feature-based face recognition in the setting where only a single
example of each face is available for training. The mixture-distance technique we introduce …

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising
results in face recognition recently. However, it remains an open question: why CNNs work …

Recently, there is a considerable amount of efforts devoted to the problem of unconstrained
face verification, where the task is to predict whether pairs of images are from the same …

The main theme of this paper is to develop a novel eigenvalue optimization framework for
learning a Mahalanobis metric. Within this context, we introduce a novel metric learning …

The goal of face identification is to decide whether two faces depict the same person or not.
This paper addresses the identification problem for face-tracks that are automatically …

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 …

We present two novel methods for face verification. Our first method-“attribute” classifiers-
uses binary classifiers trained to recognize the presence or absence of describable aspects …

This paper presents a new discriminative deep metric learning (DDML) method for face
verification in the wild. Different from existing metric learning-based face verification …

In recent years, large databases of natural images have become increasingly popular in the
evaluation of face and object recognition algorithms. However, Pinto et al. previously …