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Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

Woon Cho, Jinbeum Jang, Andreas Koschan

    Optics Express
    |December 2, 2016
    PubMed
    Summary

    Hyperspectral imaging alignment is improved using new prediction models that assess image quality. This framework enhances face recognition accuracy by selecting the best alignment method for hyperspectral data.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Biomedical Imaging

    Background:

    • Subject motion during data acquisition causes inter-band misalignment in hyperspectral imaging.
    • Assessing alignment quality is crucial for improving hyperspectral image analysis.
    • Existing alignment methods require robust quality assessment for optimal performance.

    Purpose of the Study:

    • To develop an automatic selection framework for optimal hyperspectral image alignment methods.
    • To enhance the performance of face recognition using improved hyperspectral image alignment.
    • To introduce novel qualitative prediction models for evaluating alignment quality.

    Main Methods:

    • Developed a principal curvature map-based model for full-reference band similarity evaluation.

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  • Created a cumulative probability of target colors in HSV color space model for no-reference image alignment assessment.
  • Validated models on a large-scale hyperspectral face database.

    • Main Results:

    • Proposed metrics demonstrated higher prediction accuracy in determining improved alignment compared to existing methods.
    • The framework successfully improved hyperspectral face recognition performance.
    • Both full-reference and no-reference metrics showed significant efficacy.

    Conclusions:

    • The automatic selection framework effectively identifies optimal alignment methods for hyperspectral images.
    • The developed prediction models provide accurate assessments of image alignment quality.
    • This work significantly advances hyperspectral imaging applications, particularly in face recognition.