Modeling biological face recognition with deep convolutional neural networks

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 consequently, 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 the present review, we summarize the first studies that used DCNNs to model biological face recognition. Based on this body of novel findings, we conclude that DCNNs are useful models that follow the general hierarchical organization of biological 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. Firstly, studies on face detection in DCNNs propose that elementary face-selectivity emerges automatically through feedforward processing. Secondly, studies on face identification in DCNNs suggest that experience and additional generative mechanisms facilitate this particular challenge. Taken together, as this novel computational approach enables close control of predisposition (i.e., architecture) and/or experience (i.e., training data), it may be suited to inform longstanding debates on the substrates of biological face recognition.