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The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning

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Deep Learning for Biometrics

Abstract

Face perception is critical for normal social functioning, and is mediated by a cortical network of regions in the ventral visual stream. Comparative analysis between present deep neural network architectures for biometrics and neural architectures in the human brain is necessary for developing artificial systems with human abilities. Neuroimaging research has advanced our understanding regarding the functional architecture of the human ventral face network. Here, we describe recent neuroimaging findings in three domains: (1) the macro- and microscopic anatomical features of the ventral face network in the human brain, (2) the characteristics of white matter connections, and (3) the basic computations performed by population receptive fields within face-selective regions composing this network. Then, we consider how empirical findings can inform the development of accurate computational deep neural networks for face recognition as well as shed light on computational benefits of specific neural implementational features.

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Notes

  1. 1.

    See Appendix for abbreviations and definitions.

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Acknowledgements

We thank Jesse Gomez for comments and edits of an earlier draft. This research has been funded by the following grants: NSF GRFP DGE-114747, 1RO1EY02231801-A1, and 1R01EY02391501-A1. Portions of this chapter were previously included within a paper published in the Annual Reviews of Vision Science, 2017 (http://www.annualreviews.org/doi/10.1146/annurev-vision-102016-061214).

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Appendix: Abbreviations and Definitions

Appendix: Abbreviations and Definitions

Collateral sulcus (CoS) : a primary sulcus in human ventral temporal cortex; the medial boundary of the fusiform gyrus

Cytoarchitecture : cellular organization across the six-layered cortical ribbon; a property used to parcellate brain areas from one another

Developmental prosopagnosia (DP) : an impairment in recognizing faces despite normal vision, intelligence, and socio-cognitive abilities and no history of brain damage

Fusiform face area (FFA) : once considered a homogenous face-selective area, it contains (at least) two cytoarchitectonically and functionally distinct components

Fusiform gyrus (FG) : a hominoid-specific macroanatomical structure in ventral temporal cortex that contains (at least) four cytoarchitectonic areas and multiple functional regions

FG1–4 : Labels for four cytoarchitectonic areas in the fusiform gyrus and neighboring sulci

Inferior occipital gyrus (IOG) : a gyrus that is posterior-lateral to the fusiform gyrus; considered the first processing stage of the ventral face network

Mid-fusiform sulcus (MFS) : a shallow, longitudinal sulcus bisecting the fusiform gyrus; a landmark identifying cytoarchitectonic and functional boundaries

mFus-faces/FFA-2: a face-selective region overlapping the anterior-lateral tip of the mid-fusiform sulcus, located within cytoarchitectonic area FG4, and 1–1.5 cm anterior to pFus-faces/FFA-1

Occipito-temporal sulcus (OTS) : a primary sulcus in human ventral temporal cortex; the lateral boundary of the fusiform gyrus

Parahippocampal place area (PPA) : a place-selective region in the collateral sulcus and parahippocampal cortex

pFus-faces/FFA-1: a face-selective region typically overlapping the posterior-lateral tip of the mid-fusiform sulcus, located within cytoarchitectonic area FG2, and 1–1.5 cm posterior to mFus-faces/FFA-2

Population receptive field (pRF): in fMRI, the region of visual space that stimulates a voxel

Receptive field (RF): the region of the visual field which elicits a response from a neuron

Ventral Temporal Cortex (VTC): a cortical expanse spanning the inferior aspect of the temporal lobe containing high-level visual regions involved in “what” perception and recognition.

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Grill-Spector, K., Kay, K., Weiner, K.S. (2017). The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning. In: Bhanu, B., Kumar, A. (eds) Deep Learning for Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-61657-5_1

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