Learning a similarity metric discriminatively, with application to face verification

1. Introduction

Traditional approaches to classification using discriminative methods, such as neural networks or support vector machines, generally require that all the categories be known in advance. They also require that training examples be available for all the categories. Furthermore, these methods are intrinsically limited to a fairly small number of categories (on the order of 100). Those methods are unsuitable for applications where the number of categories is very large, where the number of samples per category is small, and where only a subset of the categories is known at the time of training. Such applications include face recognition and face verification: the number of categories can be in the hundreds or thousands, with only a few examples per category. A common approach to this kind of problem is distance-based methods, which consist in computing a similarity metric between the pattern to be classified or verified and a library of stored prototypes. Another common approach is to use non-discriminative (generative) probabilistic methods in a reduced-dimension space, where the model for one category can be trained without using examples from other categories. To apply discriminative learning techniques to this kind of application, we must devise a method that can extract information about the problem from the available data, without requiring specific information about the categories.