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The Human Face as a Dynamic Tool for Social Communication
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As a highly social species, humans frequently exchange social information to support almost all facets of life. One of the richest and most powerful tools in social communication is the face, from which observers can quickly and easily make a number of inferences - about identity, gender, sex, age, race, ethnicity, sexual orientation, physical health, attractiveness, emotional state, personality traits, pain or physical pleasure, deception, and even social status. With the advent of the digital economy, increasing globalization and cultural integration, understanding precisely which face information supports social communication and which produces misunderstanding is central to the evolving needs of modern society (for example, in the design of socially interactive digital avatars and companion robots). Doing so is challenging, however, because the face can be thought of as comprising a high-dimensional, dynamic information space, and this impacts cognitive science and neuroimaging, and their broader applications in the digital economy. New opportunities to address this challenge are arising from the development of new methods and technologies, coupled with the emergence of a modern scientific culture that embraces cross-disciplinary approaches. Here, we briefly review one such approach that combines state-of-the-art computer graphics, psychophysics and vision science, cultural psychology and social cognition, and highlight the main knowledge advances it has generated. In the light of current developments, we provide a vision of the future directions in the field of human facial communication within and across cultures.
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“The third one, the dynamics, play the most important role for emotion face analysis [44].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [44]
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Metadata
- Authors
- Jack, R. E., & Schyns, P. G.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- Jack, Rachael E.
- Schyns, Philippe G.
- Venue
- Current Biology
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 25
- Issue
- 14
- Pages
- R621-R634
- ISSN
- 0960-9822 (ISSN-L)
- OpenAlex
- W1932659519
- Cited by
- 405
- References
- 198
- Citations by year
- 2026: 42 · 2025: 58 · 2024: 55 · 2023: 46 · 2022: 51 · 2021: 43 · 2020: 31 · 2019: 22 · 2018: 28 · 2017: 14 · 2016: 14
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Funders
- Medical Research Council (ESRC/MRC-060-25-0010)
- Economic and Social Research Council (ES/K001973/1, ES/K00607X/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Evolutionary Psychology and Human Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- facial expressions, facial communication, social communication, face perception, cross-cultural perception, social cognition, cultural psychology, avatars, companion robots, deception detection, emotion recognition
- MeSH terms
- Communication, Face, Female, Humans, Male
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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