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Related Experiment Video

Updated: Jun 23, 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

Algorithm for detecting human faces based on convex-hull.

Minsick Park, Chang-Woo Park, Mignon Park

    Optics Express
    |May 14, 2009
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel color face detection method using convex-hull analysis. The algorithm effectively identifies faces, even when rotated or multiple faces are present.

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    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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    Last Updated: Jun 23, 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

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
    12:39

    A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

    Published on: January 18, 2020

    Area of Science:

    • Computer Vision
    • Image Processing
    • Pattern Recognition

    Background:

    • Accurate face detection is crucial for various applications.
    • Existing methods face challenges with variations in pose and illumination.
    • Color information offers a promising avenue for robust face detection.

    Purpose of the Study:

    • To propose a new color face detection algorithm.
    • To leverage convex-hull properties for improved accuracy.
    • To handle diverse facial orientations and multiple faces within an image.

    Main Methods:

    • Detecting skin and hair-like regions in color images.
    • Applying convex-hull analysis to identified regions.
    • Utilizing the intersection of these regions to locate faces.

    Main Results:

    • The proposed method successfully detects faces in various conditions.
    • Demonstrated effectiveness with rotated and turned faces.
    • Capable of detecting multiple faces simultaneously.

    Conclusions:

    • The convex-hull based color face detection method is effective.
    • The algorithm shows robustness against pose variations.
    • This approach offers a viable solution for real-world face detection scenarios.