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Related Experiment Video

Updated: May 1, 2026

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

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Robust face recognition from multi-view videos.

Ming Du, Aswin C Sankaranarayanan, Rama Chellappa

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 12, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel feature for robust multiview face recognition using spherical harmonics. The approach effectively handles pose and lighting variations in video data, outperforming traditional methods.

    Related Experiment Videos

    Last Updated: May 1, 2026

    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

    8.6K

    Area of Science:

    • Computer Science
    • Biometrics
    • Image Processing

    Background:

    • Multiview face recognition is crucial for security and surveillance.
    • Existing methods struggle with pose and lighting variations.
    • Video data offers redundancy for improved robustness.

    Purpose of the Study:

    • To develop a novel feature for robust multiview face recognition.
    • To address challenges posed by pose and diffuse lighting variations.
    • To leverage multiview video data redundancy for enhanced accuracy.

    Main Methods:

    • Utilizing spherical harmonic representation of face texture.
    • Generating face texture maps by back-projecting multiview video data.
    • Employing reproducing kernel Hilbert space for feature similarity measurement.

    Main Results:

    • The proposed novel feature demonstrates robustness against pose and lighting variations.
    • The approach effectively exploits redundancy in multiview video data.
    • Experimental results show superior performance compared to traditional algorithms.

    Conclusions:

    • The novel spherical harmonic-based feature offers a robust solution for multiview face recognition.
    • Exploiting video data redundancy is key to handling variations.
    • This method advances the state-of-the-art in video-based face recognition systems.