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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.1K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
12.1K

You might also read

Related Articles

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

Sort by
Same author

Impact of reaction sequence on the physicochemical stability and in vitro digestibility of β-carotene-loaded emulsions stabilized by a ternary covalent complex.

Food chemistry: X·2026
Same author

VINA-SLAM: A Voxel-Based Inertial and Normal-Aligned LiDAR-IMU SLAM.

Sensors (Basel, Switzerland)·2026
Same author

Targetless LiDAR-camera extrinsic calibration via semantic distribution alignment.

Frontiers in robotics and AI·2026
Same author

Advances in the effect of the structure of high-moisture extruded plant-based meat on in vitro digestive properties.

Food research international (Ottawa, Ont.)·2026
Same author

Dual-task collaborative optimization for fundus image disease diagnosis and quality assessment.

Medical engineering & physics·2026
Same author

Bioinspired triboelectric droplet sensor for ammonia monitoring.

Nature communications·2026

Related Experiment Video

Updated: Jan 10, 2026

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.9K

Low-resolution driver face recognition based on super-resolution and triplet loss.

Zhi Zhang1,2, Bingyu Sun3, Jiuzhen Liang4

  • 1Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui, China.

Scientific Reports
|November 27, 2025
PubMed
Summary

This study introduces a new driver face recognition model (SPFL-DC) for challenging traffic scenarios. It automates dataset creation and uses super-resolution to improve low-resolution face recognition.

Keywords:
Face recognitionFeature fusionSelf-constructed datasetSuper-resolutionTriplet loss

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996

Related Experiment Videos

Last Updated: Jan 10, 2026

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

996

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Deep neural networks excel at face recognition but struggle in unconstrained, low-resolution scenarios like traffic monitoring.
  • Vehicle-based face recognition faces challenges due to image variations and limited computational resources.

Purpose of the Study:

  • To develop a robust low-resolution driver face detection and recognition model (SPFL-DC) for complex traffic environments.
  • To enhance dataset construction efficiency and model prediction robustness for unconstrained face recognition.

Main Methods:

  • Automated dataset construction using triplet loss, guided by license plate information and pre-training on public datasets.
  • A super-resolution technique to process and fuse low-resolution images, preserving critical identity information.
  • Experimental validation on AR and LFW datasets.

Main Results:

  • The SPFL-DC framework demonstrates effective low-resolution driver face detection and recognition.
  • Automated dataset construction significantly improves efficiency and model robustness.
  • Super-resolution fusion enhances image quality while retaining essential identity features.

Conclusions:

  • The SPFL-DC framework offers a competitive solution for face recognition in challenging, resource-constrained traffic monitoring systems.
  • The novel dataset construction and super-resolution methods improve the performance and applicability of deep learning models in real-world scenarios.