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Related Experiment Video

Updated: Jun 6, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

Published on: April 8, 2019

Robust Facial Feature Tracking Using Shape-Constrained Multiresolution-Selected Linear Predictors.

Eng-Jon Ong, Richard Bowden

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 8, 2010
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a data-driven method for real-time facial feature tracking using intensity information. The approach accurately tracks facial points without manual input, outperforming existing methods with minimal training data.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Biomedical Imaging

    Background:

    • Facial feature tracking is challenging due to face deformation and texture variations.
    • Existing methods often require manual feature selection or strong visual cues.
    • A data-driven approach is needed for robust and automatic facial tracking.

    Purpose of the Study:

    • To propose a learned, data-driven method for accurate, real-time facial feature tracking.
    • To automatically identify optimal visual support for tracking individual facial feature points.
    • To develop a robust system that can track any point on the face.

    Main Methods:

    • Utilizes linear predictors (LPs) for mapping pixel intensities to feature displacements.
    • Introduces a biased linear predictor and groups LPs into rigid flocks for robustness.

    Related Experiment Videos

    Last Updated: Jun 6, 2026

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
    05:57

    Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

    Published on: April 8, 2019

  • Employs a probabilistic selection method and hierarchical multiresolution LP model for accuracy.
  • Incorporates a shape constraint to correct occasional tracking failures.

    • Main Results:

    • The proposed method achieves accurate and robust real-time facial feature tracking.
    • Outperforms Active Appearance Models (AAMs) with minimal training data.
    • Demonstrates consistent visual support analysis across different subjects.
    • Effective on sequences from standard definition to YouTube quality.

    Conclusions:

    • The developed method offers a significant advancement in automatic facial feature tracking.
    • Its data-driven, intensity-based approach reduces reliance on manual design and specific feature points.
    • The system shows high accuracy and robustness, suitable for diverse video qualities and subjects.