Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

doi:10.1146/annurev-vision-093019-111701

Paper

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

Published Aug 4, 2021 · A. O’toole, C. Castillo

Annual review of vision science

Q1 SJR score

44

Citations

0

Influential Citations

PDF

Semantic Scholar

Abstract

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Study Snapshot

Key takeawayDeep learning models achieve human-level performance on real-world face recognition tasks, highlighting the need for diverse learning types to model human face processing skills and cross-race effects.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Sign up to use Study Snapshot

Consensus is limited without an account. Create an account or sign in to get more searches and use the Study Snapshot.

Create an account

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.

Sign up to use Study Snapshot

References

Citations