  Face recognition has long been an active research area in the field of
artificial intelligence, particularly since the rise of deep learning in recent
years. In some practical situations, each identity has only a single sample
available for training. Face recognition under this situation is referred to as
single sample face recognition and poses significant challenges to the
effective training of deep models. Therefore, in recent years, researchers have
attempted to unleash more potential of deep learning and improve the model
recognition performance in the single sample situation. While several
comprehensive surveys have been conducted on traditional single sample face
recognition approaches, emerging deep learning based methods are rarely
involved in these reviews. Accordingly, we focus on the deep learning-based
methods in this paper, classifying them into virtual sample methods and generic
learning methods. In the former category, virtual images or virtual features
are generated to benefit the training of the deep model. In the latter one,
additional multi-sample generic sets are used. There are three types of generic
learning methods: combining traditional methods and deep features, improving
the loss function, and improving network structure, all of which are covered in
our analysis. Moreover, we review face datasets that have been commonly used
for evaluating single sample face recognition models and go on to compare the
results of different types of models. Additionally, we discuss problems with
existing single sample face recognition methods, including identity information
preservation in virtual sample methods, domain adaption in generic learning
methods. Furthermore, we regard developing unsupervised methods is a promising
future direction, and point out that the semantic gap as an important issue
that needs to be further considered.
