Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Facial Feedback Hypothesis

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

Long-Term Trends in the Burden of Bipolar Disorder in China, 1990-2052: An Analysis of the Global Burden of Disease Study 2023.

Psychology research and behavior management·2026

Same author

A risk-based post ablation follow-up strategy for hepatocellular carcinoma.

JHEP reports : innovation in hepatology·2026

Same author

XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

MMA++: Effective Multi-Modal Adaptation for Vision-Language Models.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Enhancing Lesion Segmentation via Medical Image-Mask Pair Synthesis using Phenotype-Conditioned Diffusion Models.

IEEE journal of biomedical and health informatics·2026

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Large Volume, Behaviorally-relevant Illumination for Optogenetics in Non-human Primates

Large Volume, Behaviorally-relevant Illumination for Optogenetics in Non-human Primates

Published on: October 3, 2017

Normalization of face illumination based on large-and small-scale features.

Xiaohua Xie¹, Wei-Shi Zheng, Jianhuang Lai

¹School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, China. sysuxiexh@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 9, 2010

Summary

This summary is machine-generated.

This study introduces a new method for face recognition by normalizing both large-scale and small-scale features. This approach significantly improves face recognition performance and visual quality compared to existing techniques.

More Related Videos

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Related Experiment Videos

Last Updated: Jun 6, 2026

Large Volume, Behaviorally-relevant Illumination for Optogenetics in Non-human Primates

Large Volume, Behaviorally-relevant Illumination for Optogenetics in Non-human Primates

Published on: October 3, 2017

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Published on: May 20, 2013

Area of Science:

Computer Vision
Image Processing
Biometrics

Background:

Face recognition systems are challenged by illumination variations.
Current methods often ignore large-scale features, focusing only on small-scale features for invariance.
This leads to suboptimal performance as large-scale features contain crucial intrinsic information.

Purpose of the Study:

To propose a novel method for face normalization that considers both large- and small-scale features.
To demonstrate that normalizing large-scale features is key for effective illumination handling.
To enhance face recognition accuracy and visual quality through improved normalization.

Main Methods:

Decomposition of face images into large-scale (low-frequency) and small-scale (high-frequency) features.
Illumination normalization primarily applied to large-scale features, with minor adjustments to small-scale features.
Reconstruction of a normalized face image by combining processed features, with an optional visual compensation step.

Main Results:

The proposed Small-and Large-scale (S&L) feature normalization method significantly improves face recognition performance.
Experimental results on CMU-PIE, Extended Yale B, and FRGC 2.0 databases show superior results compared to state-of-the-art methods.
Enhanced visual quality of the normalized face images was observed.

Conclusions:

Both large-scale and small-scale features are vital for robust face recognition and restoration.
Targeted illumination normalization on large-scale features is more effective than normalizing the entire image.
The S&L method offers a significant advancement in face recognition technology.