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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.3K
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,...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Mixed Reality and Desktop Hand Hygiene Training With Deep Learning-Based Step Recognition and Real-Time Decision Support.

IEEE transactions on visualization and computer graphics·2026
Same author

Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease.

Journal of clinical medicine·2025
Same author

Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam.

Foods (Basel, Switzerland)·2025
Same author

Digital Mapping of Central Asian Foods: Towards a Standardized Visual Atlas for Nutritional Research.

Nutrients·2025
Same author

Improved food image recognition by leveraging deep learning and data-driven methods with an application to Central Asian Food Scene.

Scientific reports·2025
Same author

A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face Recognition with Applications.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jul 1, 2025

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

9.4K

Faces in Event Streams (FES): An Annotated Face Dataset for Event Cameras.

Ulzhan Bissarinova1, Tomiris Rakhimzhanova1, Daulet Kenzhebalin1

  • 1Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

Researchers created the first large dataset for event-based camera face detection. This enables new computer vision applications by providing 689 minutes of annotated event streams for faces and facial landmarks.

Keywords:
computer visionevent cameraevent streamface detectionfacial landmark detection

More Related Videos

Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

696
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Related Experiment Videos

Last Updated: Jul 1, 2025

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

9.4K
Profiling Maternal Behavior Responses During Whole-Brain Imaging
07:12

Profiling Maternal Behavior Responses During Whole-Brain Imaging

Published on: January 24, 2025

696
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

Area of Science:

  • Computer Vision
  • Robotics
  • Biometrics

Background:

  • Event-based cameras offer advantages like high dynamic range and low latency for computer vision tasks.
  • Existing datasets for face and facial landmark detection using event cameras are limited in size and scope, hindering research progress.
  • A significant gap exists in large-scale, annotated event stream datasets for facial analysis.

Purpose of the Study:

  • To introduce the first large and diverse dataset, Faces in Event Streams (FiES), for face and facial landmark detection using event-based camera data.
  • To provide a valuable resource for advancing research and development in event-based facial recognition and analysis.
  • To facilitate the creation of novel computer vision applications leveraging event camera technology.

Main Methods:

  • The study introduces the Faces in Event Streams (FiES) dataset, comprising 689 minutes of raw event data.
  • The dataset includes detailed annotations for face bounding boxes and facial landmarks.
  • Twelve distinct models were trained and evaluated on the FiES dataset for face and landmark detection tasks.

Main Results:

  • The newly published Faces in Event Streams dataset is the first of its kind, offering extensive annotated data for event-based facial analysis.
  • Models trained on the FiES dataset achieved a mean Average Precision (mAP) score exceeding 90% for bounding box and facial landmark prediction.
  • Real-time face detection using event-based cameras was successfully demonstrated with the developed models.

Conclusions:

  • The Faces in Event Streams dataset addresses a critical need for large-scale annotated data in event-based computer vision.
  • The high performance of the trained models demonstrates the dataset's effectiveness in advancing face and facial landmark detection.
  • This work paves the way for enhanced real-time facial analysis applications utilizing event-based cameras.