ReviewAnnual review of vision science2021
Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.
Review in Annual review of vision science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed.
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- The outcome prediction method of football matches by the quantum neural network based on deep learning.Scientific reports · 2025Article
- Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty.Computational brain & behavior · 2025Article
- Configural processing as an optimized strategy for robust object recognition in neural networks.Communications biology · 2025Article
- Deep convolutional neural networks are sensitive to face configuration.Journal of vision · 2024Article
- Using deep neural networks to disentangle visual and semantic information in human perception and memory.Nature human behaviour · 2024Article
- The Emerging Science of Interacting Minds.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Decoding face recognition abilities in the human brain.PNAS nexus · 2024Article
- Disentangled deep generative models reveal coding principles of the human face processing network.PLoS computational biology · 2024Article
- Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.Cell reports · 2024Article
- Distinct patterns of neural response to faces from different races in humans and deep networks.Social cognitive and affective neuroscience · 2023Article
- The neural code for "face cells" is not face-specific.Science advances · 2023Article
- Twin Identification over Viewpoint Change: A Deep Convolutional Neural Network Surpasses Humans.ACM transactions on applied perception · 2023Article
- Unsupervised learning reveals interpretable latent representations for translucency perception.PLoS computational biology · 2023Article
- Is increased activation in the fusiform face area to Greebles a result of appropriate expertise training or caused by Greebles' face likeness?Frontiers in neuroscience · 2023Article
- Early experience with low-pass filtered images facilitates visual category learning in a neural network model.PloS one · 2023Article
- Using AAEHS-Net as an Attention-Based Auxiliary Extraction and Hybrid Subsampled Network for Semantic Segmentation.Computational intelligence and neuroscience · 2022Article
- Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition.Journal of vision · 2021Article
- Perceptual Expertise of Forensic Examiners and Reviewers on Tests of Cross-Race and Disguised Face Identification and Face Memory.Applied cognitive psychologyArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.