Jove
Visualize
Contact Us

Related Concept Videos

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

139
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...
139
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
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,...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Fatigue in ride-hailing and delivery workers in Indonesia: a scoping review and multi-stakeholder workshop.

Ergonomics·2025
Same author

A two-stage improved variable neighborhood search-sine cosine algorithm for the multi-row layout problem with safety consideration.

Scientific reports·2025
Same author

Detecting fake news during COVID-19 in Indonesia: the role of trust level.

Journal of communication in healthcare·2023
Same author

How humans adapt to hot climates learned from the recent research on tropical indigenes.

Journal of physiological anthropology·2022
Same author

Exploring the mediation role of employees' well-being in the relationship between psychosocial factors and musculoskeletal pain during the COVID-19 pandemic.

Work (Reading, Mass.)·2021
Same author

Cutaneous Warm and Cool Sensation Thresholds and the Inter-threshold Zone in Malaysian and Japanese Males.

Journal of thermal biology·2017
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 Experiment Video

Updated: Jun 23, 2025

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

2.7K

Image-based facial emotion recognition using convolutional neural network on emognition dataset.

Erlangga Satrio Agung1, Achmad Pratama Rifai2, Titis Wijayanto1

  • 1Department of Mechanical and Industrial Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia.

Scientific Reports
|June 23, 2024
PubMed
Summary

This study enhances facial emotion recognition (FER) using deep learning on the Emognition dataset. The proposed Convolutional Neural Network (CNN) models achieve high accuracy in detecting ten emotions from facial images.

Keywords:
Convolutional neural networkDeep learningEmognition datasetFacial emotion recognition

More Related Videos

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.0K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.1K

Related Experiment Videos

Last Updated: Jun 23, 2025

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

2.7K
Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.0K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Facial emotion recognition (FER) is challenging due to expression variability.
  • Existing deep learning FER models are limited by restricted datasets.
  • The Emognition dataset offers a broader range of ten target emotions.

Purpose of the Study:

  • To expand deep learning applications for FER.
  • To develop robust Convolutional Neural Network (CNN) models for classifying ten emotions.
  • To evaluate model performance using transfer learning and custom architectures.

Main Methods:

  • Data preprocessing: video to image conversion and data augmentation.
  • Model development: transfer learning (Inception-V3, MobileNet-V2) and custom CNNs.
  • Hyperparameter optimization using the Taguchi method for robust model building.

Main Results:

  • Achieved high performance on the test dataset.
  • Demonstrated an accuracy of 96%.
  • Obtained an average F1-score of 0.95.

Conclusions:

  • The proposed CNN models show significant potential for accurate FER.
  • The study validates the effectiveness of deep learning on diverse emotion datasets.
  • This research contributes to advancing the field of automated emotion detection.