  Deep convolutional neural networks (DCNNs) have become the state-of-the-art
computational models of biological object recognition. Their remarkable success
has helped vision science break new ground and recent efforts have started to
transfer this achievement to research on biological face recognition. In this
regard, face detection can be investigated by comparing face-selective
biological neurons and brain areas to artificial neurons and model layers.
Similarly, face identification can be examined by comparing in vivo and in
silico multidimensional "face spaces". In this review, we summarize the first
studies that use DCNNs to model biological face recognition. On the basis of a
broad spectrum of behavioral and computational evidence, we conclude that DCNNs
are useful models that closely resemble the general hierarchical organization
of face recognition in the ventral visual pathway and the core face network. In
two exemplary spotlights, we emphasize the unique scientific contributions of
these models. First, studies on face detection in DCNNs indicate that
elementary face selectivity emerges automatically through feedforward
processing even in the absence of visual experience. Second, studies on face
identification in DCNNs suggest that identity-specific experience and
generative mechanisms facilitate this particular challenge. Taken together, as
this novel modeling approach enables close control of predisposition (i.e.,
architecture) and experience (i.e., training data), it may be suited to inform
long-standing debates on the substrates of biological face recognition.
