Efficient inverse graphics in biological face processing

doi:10.1126/sciadv.aax5979

Paper

Efficient inverse graphics in biological face processing

Published Apr 2, 2018 · Ilker Yildirim, M. Belledonne, W. Freiwald

Science Advances

Q1 SJR score

94

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2

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Abstract

Neural networks in the primate brain may invert a graphics style model of how 3D object shapes and textures cause observed images. Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.

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Study Snapshot

Key takeawayThis efficient inverse graphics model explains human behavior and maps directly onto a specialized face-processing circuit in the primate brain, improving face recognition performance.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Efficient inverse graphics in biological face processing

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