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First impressions from faces in the real world: Commentary on Sutherland and Young (2022)

The British journal of psychology, 2023-05, Vol.114 (2), p.508-510

2023

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Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?

IEEE transactions on biometrics, behavior, and identity science, 2021-01, Vol.3 (1), p.101-111

2021

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The neural processing of familiar and unfamiliar faces: A review and synopsis

The British journal of psychology, 2011-11, Vol.102 (4), p.726-747

2011

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Perceptual expertise in forensic facial image comparison

Proceedings of the Royal Society. B, Biological sciences, 2015-09, Vol.282 (1814), p.20151292-20151292

2015

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

Annual review of vision science, 2021-09, Vol.7 (1), p.543-570

2021

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Collaboration to improve cross-race face identification: Wisdom of the multi-racial crowd?

The British journal of psychology, 2023-11, Vol.114 (4), p.838-853

2023

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Facial expression is retained in deep networks trained for face identification

Journal of vision (Charlottesville, Va.), 2021-04, Vol.21 (4), p.4-4

2021

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Dissecting the time course of person recognition in natural viewing environments

The British journal of psychology, 2016-02, Vol.107 (1), p.117-134

2016

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Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition

Journal of vision (Charlottesville, Va.), 2021-08, Vol.21 (8), p.15-15

2021

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The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks

ACM transactions on applied perception, 2024-01, Vol.21 (1), p.1-13, Article 2

2024

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Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

Journal of cognitive neuroscience, 2007-11, Vol.19 (11), p.1735-1752

2007

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Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

Proceedings of the National Academy of Sciences - PNAS, 2018-06, Vol.115 (24), p.6171-6176

2018

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Face recognition ability does not predict person identification performance: using individual data in the interpretation of group results

Cognitive research: principles and implications, 2018-06, Vol.3 (1), p.23-23, Article 23

2018

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Twin Identification over Viewpoint Change: A Deep Convolutional Neural Network Surpasses Humans

ACM transactions on applied perception, 2023-07, Vol.20 (3), p.1-15, Article 10

2023

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Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex

Journal of cognitive neuroscience, 2005-04, Vol.17 (4), p.580-590

2005

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Wisdom of the social versus non‐social crowd in face identification

The British journal of psychology, 2018-11, Vol.109 (4), p.724-735

2018

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Dissociable Neural Patterns of Facial Identity across Changes in Viewpoint

Journal of cognitive neuroscience, 2010-07, Vol.22 (7), p.1570-1582

2010

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An other-race effect for face recognition algorithms

ACM transactions on applied perception, 2011-01, Vol.8 (2), p.1-11

2011

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Comparing face recognition algorithms to humans on challenging tasks

ACM transactions on applied perception, 2012-10, Vol.9 (4), p.1-13

2012

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Integrating faces and bodies in social trait perception

Journal of vision (Charlottesville, Va.), 2022-12, Vol.22 (14), p.3942

2022

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Does confidence predict face-identification accuracy for same-race and other-race faces?

Journal of vision (Charlottesville, Va.), 2024-09, Vol.24 (10), p.393

2024

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Modeling face-Identity “likeness” with a convolutional neural network trained for face identification

Journal of vision (Charlottesville, Va.), 2024-09, Vol.24 (10), p.1292

2024

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Deep convolutional neural networks are sensitive to configural properties of faces

Journal of vision (Charlottesville, Va.), 2023-08, Vol.23 (9), p.5560

2023

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People can evaluate the correctness of their face-identification decisions using comparative confidence judgments

Journal of vision (Charlottesville, Va.), 2022-12, Vol.22 (14), p.3341

2022

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Representation of Face Shape and Surface Reflectance in Deep Convolutional Neural Networks

Journal of vision (Charlottesville, Va.), 2022-12, Vol.22 (14), p.4369

2022

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First impressions from faces in the real world: Commentary on Sutherland and Young (2022)

Accuracy Comparison Across Face Recognition Algorithms: Where Are We on Measuring Race Bias?

The neural processing of familiar and unfamiliar faces: A review and synopsis

Perceptual expertise in forensic facial image comparison

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

Collaboration to improve cross-race face identification: Wisdom of the multi-racial crowd?

Facial expression is retained in deep networks trained for face identification

Dissecting the time course of person recognition in natural viewing environments

Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition

The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks

Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

Face recognition ability does not predict person identification performance: using individual data in the interpretation of group results

Twin Identification over Viewpoint Change: A Deep Convolutional Neural Network Surpasses Humans

Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex

Wisdom of the social versus non‐social crowd in face identification

Dissociable Neural Patterns of Facial Identity across Changes in Viewpoint

An other-race effect for face recognition algorithms

Comparing face recognition algorithms to humans on challenging tasks

Integrating faces and bodies in social trait perception

Does confidence predict face-identification accuracy for same-race and other-race faces?

Modeling face-Identity “likeness” with a convolutional neural network trained for face identification

Deep convolutional neural networks are sensitive to configural properties of faces

People can evaluate the correctness of their face-identification decisions using comparative confidence judgments

Representation of Face Shape and Surface Reflectance in Deep Convolutional Neural Networks

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