  One-Class Classification (OCC) is a special case of multi-class
classification, where data observed during training is from a single positive
class. The goal of OCC is to learn a representation and/or a classifier that
enables recognition of positively labeled queries during inference. This topic
has received considerable amount of interest in the computer vision, machine
learning and biometrics communities in recent years. In this article, we
provide a survey of classical statistical and recent deep learning-based OCC
methods for visual recognition. We discuss the merits and drawbacks of existing
OCC approaches and identify promising avenues for research in this field. In
addition, we present a discussion of commonly used datasets and evaluation
metrics for OCC.
