Modeling biological face recognition with deep convolutional neural networks

TL;DR

This paper reviews how deep convolutional neural networks (DCNNs) serve as effective models for biological face recognition, closely mimicking neural processes and offering insights into face detection and identification mechanisms.

Contribution

It summarizes pioneering studies demonstrating DCNNs' ability to model biological face recognition, highlighting their hierarchical similarity to the ventral visual pathway and core face network.

Findings

DCNNs automatically develop face selectivity without visual experience.

Experience and generative mechanisms enhance face identification in DCNNs.

DCNNs closely resemble neural organization of biological face recognition.

Abstract

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.