# Face processing emerges from object-trained convolutional neural networks

arXiv: https://arxiv.org/abs/2405.18800 · HTML: https://mlsanity.com/paper/2405.18800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 106 references that are in the map.
- First posted on arXiv 29 May 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [A Study of Face Obfuscation in ImageNet](https://mlsanity.com/paper/2103.06191/) (2021)
- [GACT: Activation Compressed Training for Generic Network Architectures](https://mlsanity.com/paper/2206.11357/) (2022)
- [AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces](https://mlsanity.com/paper/1909.04951/) (2019)
- [Eigen-Distortions of Hierarchical Representations](https://mlsanity.com/paper/1710.02266/) (2017)
- [Extracting low-dimensional psychological representations from convolutional neural networks](https://mlsanity.com/paper/2005.14363/) (2020)
- [DenseNet for Dense Flow](https://mlsanity.com/paper/1707.06316/) (2017)

## Abstract (arXiv)

Whether face processing depends on unique, domain-specific neurocognitive mechanisms or domain-general object recognition mechanisms has long been debated. Directly testing these competing hypotheses in humans has proven challenging due to extensive exposure to both faces and objects. Here, we systematically test these hypotheses by capitalizing on recent progress in convolutional neural networks (CNNs) that can be trained without face exposure (i.e., pre-trained weights). Domain-general mechanism accounts posit that face processing can emerge from a neural network without specialized pre-training on faces. Consequently, we trained CNNs solely on objects and tested their ability to recognize and represent faces as well as objects that look like faces (face pareidolia stimuli).... Due to the character limits, for more details see in attached pdf