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 Experiment Videos

Local evidence aggregation for regression-based facial point detection.

Brais Martinez1, Michel F Valstar, Xavier Binefa

  • 1Department of Computing, Imperial College London, London, United Kingdom. b.martinez@imperial.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 23, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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,...

You might also read

Related Articles

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

Sort by
Same author

The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech.

Frontiers in digital health·2025
Same author

Research in methodologies for modelling the oral cavity.

Biomedical physics & engineering express·2024
Same author

Benchmarking framework for machine learning classification from fNIRS data.

Frontiers in neuroergonomics·2024
Same author

Self-Supervised Video-Centralised Transformer for Video Face Clustering.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Look-alike humans identified by facial recognition algorithms show genetic similarities.

Cell reports·2022
Same author

End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks.

IEEE transactions on cybernetics·2022
Same journal

A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Robust 3D Semantic Occupancy Prediction With Calibration-Free Spatial Transformation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Image Restoration via Multi-domain Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a novel facial point detection algorithm. It enhances accuracy and robustness by combining regression with a face shape model, improving upon existing methods.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Facial point detection is crucial for various applications like facial recognition and analysis.
  • Existing regression-based methods often struggle with accuracy and robustness.
  • Probabilistic graphical models offer a way to incorporate shape constraints.

Purpose of the Study:

  • To develop a novel algorithm for accurate facial point detection in frontal and near-frontal images.
  • To improve the robustness and efficiency of facial point detection systems.
  • To combine the strengths of regression-based approaches and shape models.

Main Methods:

  • A hybrid approach combining a regression-based method with a probabilistic graphical model-based face shape model.
  • Stochastic selection of local appearance information for robust prediction aggregation.

Related Experiment Videos

  • Integration of a quality measure for prediction assessment and shape model for region correction.

    • Main Results:

    • The algorithm demonstrated significant improvements over current state-of-the-art methods.
    • Tested on over 7,500 images across five databases, achieving high accuracy.
    • The combined approach balanced computational efficiency with enhanced robustness.

    Conclusions:

    • The proposed algorithm offers a robust and computationally efficient solution for facial point detection.
    • The integration of shape models effectively restricts search regions and corrects predictions.
    • This method represents a significant advancement in facial analysis technology.