Evidence map›Paper›PMID 33664365›Full record

ArticleScientific reports2021

Demographic effects on facial emotion expression: an interdisciplinary investigation of the facial action units of happiness.

Yingruo Fan, Jacqueline C K Lam, Victor O K Li

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Facial Expressions as a Nexus for Health Assessment.Bioengineering (Basel, Switzerland) · 2026
    Review
  2. Article
  3. Article
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  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yingruo Fan *Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China.
Jacqueline C K Lam *Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China. jcklam@eee.hku.hk.
Victor O K LiDepartment of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China. vli@eee.hku.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding demographic difference in facial expression of happiness has crucial implications on social communication. However, prior research on facial emotion expression has mostly focused on the effect of a single demographic factor (typically gender, race, or age), and is limited by the small image dataset collected in laboratory settings. First, we used 30,000 (4800 after pre-processing) real-world facial images from Flickr, to analyze the facial expression of happiness as indicated by the intensity level of two distinctive facial action units, the Cheek Raiser (AU6) and the Lip Corner Puller (AU12), obtained automatically via a deep learning algorithm that we developed, after training on 75,000 images. Second, we conducted a statistical analysis on the intensity level of happiness, with both the main effect and the interaction effect of three core demographic factors on AU12 and AU6. Our results show that females generally display a higher AU12 intensity than males. African Americans tend to exhibit a higher AU6 and AU12 intensity, when compared with Caucasians and Asians. The older age groups, especially the 40-69-year-old, generally display a stronger AU12 intensity than the 0-3-year-old group. Our interdisciplinary study provides a better generalization and a deeper understanding on how different gender, race and age groups express the emotion of happiness differently.

Indexed as

Facial ExpressionHappinessAdultAgedAngerCheekDeep LearningEmotionsFaceFacial RecognitionFemaleHumansMaleMiddle Aged

Identifiers

PMID33664365
PMCPMC7970860

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.