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Thermal-to-visible face recognition using partial least squares.

Shuowen Hu, Jonghyun Choi, Alex L Chan

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
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    Summary
    This summary is machine-generated.

    This study introduces a partial least squares (PLS) approach for thermal-to-visible face recognition, overcoming challenges posed by differing image modalities. The method effectively reduces the modality gap, enabling robust cross-modal face recognition in diverse scenarios.

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    Area of Science:

    • Biometrics
    • Computer Vision
    • Machine Learning

    Background:

    • Visible face recognition is well-established, but cross-modal face recognition, particularly thermal-to-visible, remains a significant challenge due to distinct imaging physics.
    • The difference in phenomenology between thermal and visible light presents a substantial hurdle for accurate face identification across these modalities.

    Purpose of the Study:

    • To develop and evaluate a robust partial least squares (PLS) regression-based approach for thermal-to-visible face recognition.
    • To address the modality gap between thermal and visible facial signatures through advanced preprocessing and feature extraction techniques.
    • To enhance cross-modal recognition performance by incorporating multi-modal information within the PLS model building framework.

    Main Methods:

    • A partial least squares (PLS) regression-based method was employed, involving distinct preprocessing and feature extraction stages.
    • Techniques were implemented to minimize the modality gap between thermal and visible facial data.
    • A one-vs-all PLS model building strategy was utilized, incorporating multi-modal information for improved recognition.

    Main Results:

    • The proposed algorithm demonstrated robust performance across three challenging datasets featuring visible and thermal imagery.
    • Evaluations covered diverse experimental scenarios including time-lapse, physical tasks, mental tasks, and varying subject-to-camera ranges.
    • The method proved effective in overcoming the inherent difficulties of thermal-to-visible face recognition.

    Conclusions:

    • The developed PLS-based approach offers a viable solution for challenging thermal-to-visible face recognition tasks.
    • The preprocessing and feature extraction methods successfully reduced the modality gap, facilitating accurate cross-modal identification.
    • The findings highlight the potential of the proposed method for real-world applications requiring robust cross-modal face recognition.