# Modeling biological face recognition with deep convolutional neural networks

arXiv: https://arxiv.org/abs/2208.06681 · HTML: https://mlsanity.com/paper/2208.06681/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 143 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Morphable Face Models - An Open Framework](https://mlsanity.com/paper/1709.08398/) (2017)
- [Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway](https://mlsanity.com/paper/1411.6422/) (2014)
- [Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition](https://mlsanity.com/paper/1406.3284/) (2014)
- [Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks](https://mlsanity.com/paper/1810.03856/) (2018)
- [Unsupervised Learning from Video with Deep Neural Embeddings](https://mlsanity.com/paper/1905.11954/) (2019)
- [Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition](https://mlsanity.com/paper/1508.03929/) (2015)
- [Triplet Probabilistic Embedding for Face Verification and Clustering](https://mlsanity.com/paper/1604.05417/) (2016)
- [Multi-view Face Detection Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1502.02766/) (2015)
- [Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders](https://mlsanity.com/paper/2110.13911/) (2021)
- [Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements](https://mlsanity.com/paper/2206.10587/) (2022)
- [A Fast Face Detection Method via Convolutional Neural Network](https://mlsanity.com/paper/1803.10103/) (2018)

## Built on by (most cited first, 1 of 1)

- [Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives](https://mlsanity.com/paper/2605.12619/) (2026)

## Abstract (arXiv)

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.