ArticleCurrent directions in psychological science2017
Computational Models of Face Perception.
Article in Current directions in psychological science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Training reduces error in rating the intensity of emotions.Emotion (Washington, D.C.) · 2022Trial
- Children's and Adolescent's Use of Context in Judgments of Emotion Intensity.Affective science · 2025Article
- Advancing Naturalistic Affective Science with Deep Learning.Affective science · 2023Review
- What is missing in the study of emotion expression?Frontiers in psychology · 2023Review
- Discriminant Functional Learning of Color Features for the Recognition of Facial Action Units and Their Intensities.IEEE transactions on pattern analysis and machine intelligence · 2019Article
- The promises and perils of automated facial action coding in studying children's emotions.Developmental psychology · 2019Article
- The neural representation of facial-emotion categories reflects conceptual structure.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements.Psychological science in the public interest : a journal of the American Psychological Society · 2019Review
- A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image.IEEE transactions on pattern analysis and machine intelligence · 2018Article
- Learning Facial Action Units from Web Images with Scalable Weakly Supervised Clustering.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Faces are one of the most important means of communication in humans. For example, a short glance at a person's face provides information on identity and emotional state. What are the computations the brain uses to solve these problems so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research studies the hypothesis that this algorithm is tasked to solve the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading image features that are invariant to facial expression, pose and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose and illumination. If one defines the physics equations that render an image under different identities, expressions, poses and illuminations, then gaining invariance to these factors is readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust and efficient as humans.
Indexed as
Identifiers
What OpenQuestion holds
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.