Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Muscles for Facial Expressions01:14

Muscles for Facial Expressions

The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cost of hospital admission after autologous breast reconstruction and factors associated with increased cost: a population-based cohort study.

Canadian journal of surgery. Journal canadien de chirurgie·2026
Same author

Perioperative Takotsubo Syndrome Following Breast Prosthesis Explantation and Capsulectomy: A Case Report.

Plastic surgery (Oakville, Ont.)·2026
Same author

The Effects of Gum Chewing in the Postoperative-Period: A Systematic Review and Meta-Analysis.

ANZ journal of surgery·2025
Same author

First-Time Use of Diced Cartilage Glue Grafts for Post Trauma Orbital Rim Reconstruction.

Plastic surgery (Oakville, Ont.)·2025
Same author

Closed-Incision Negative Pressure Therapy Compared to Conventional Dressing Following Autologous Abdominal Tissue Breast Reconstruction: The MACVAC Pilot Randomized Control Trial.

Plastic surgery (Oakville, Ont.)·2025
Same author

Factors associated with emergency free flap reoperation in postmastectomy breast reconstruction: A population-based cohort study.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS·2025

Related Experiment Video

Updated: Jun 9, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.5K

"Facekit"-Toward an Automated Facial Analysis App Using a Machine Learning-Derived Facial Recognition Algorithm.

Omri Nachmani1, Tomas Saun2, Minh Huynh3

  • 1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.

Plastic Surgery (Oakville, Ont.)
|November 2, 2023
PubMed
Summary
This summary is machine-generated.

Smartphone facial analysis tools show low accuracy for clinical use. Current machine learning algorithms, like the one in Facekit, do not reliably measure facial proportions compared to direct or digital methods.

Keywords:
cosmeticfacial analysisfacial cannonsfacial recognitionmachine learning

More Related Videos

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Related Experiment Videos

Last Updated: Jun 9, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.5K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.8K

Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Medical Imaging

Background:

  • Facial recognition machine learning offers potential for facial feature analysis.
  • Clinical applications in plastic surgery require high measurement accuracy.
  • Smartphone apps can accelerate the development and testing of such tools.

Purpose of the Study:

  • To compare the accuracy of a smartphone-based facial recognition algorithm (Facekit) against direct and digital measurements.
  • To evaluate the clinical utility of open-access machine learning tools for facial analysis.

Main Methods:

  • Developed Facekit, an Android application using Google's ML Kit computer vision API.
  • Measured four facial proportions in 15 healthy subjects.
  • Compared smartphone measurements with direct surface and digital measurements using intraclass correlation (ICC) and Pearson correlation.

Main Results:

  • The highest ICC for naso-facial proportion was 0.321, far below the excellent agreement threshold of 0.75.
  • Significant differences were found between ML Kit, direct, and digital measurement methods (P < .05).
  • Facekit measurements showed low correlation and agreement (R < 0.5, ICC < 0.75) with direct and digital methods for all tested ratios.

Conclusions:

  • The Facekit application demonstrates low agreement with established measurement techniques.
  • The current pre-trained facial recognition software lacks the accuracy needed for clinical facial analysis in plastic surgery.
  • Developing custom machine learning models trained on specific clinical landmarks may improve performance.