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Face Recognition With Pose Variations and Misalignment via Orthogonal Procrustes Regression.

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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces orthogonal Procrustes regression (OPR) to improve 2D face recognition by addressing pose variations. The novel stacked OPR model enhances accuracy in handling complex pose changes for better face recognition systems.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Linear regression methods are popular in face recognition but struggle with pose variations.
    • Sparse and collaborative representation classifiers have gained attention but are sensitive to pose changes.
    • Existing methods often require separate steps for face alignment and pose correction.

    Purpose of the Study:

    • To introduce a novel model, orthogonal Procrustes regression (OPR), to effectively handle pose variations in 2D face recognition.
    • To develop a stacked OPR model capable of addressing highly non-linear pose variations.
    • To create a unified framework for face alignment, pose correction, and representation.

    Main Methods:

    • Introduced the orthogonal Procrustes problem (OPP) to model optimal linear transformations between images with different poses.
    • Integrated OPP into a regression framework to propose the orthogonal Procrustes regression (OPR) model.
    • Developed a stacked OPR by adopting a progressive strategy to handle non-linear pose variations.
    • Optimized the OPR model using an efficient alternating iterative algorithm.

    Main Results:

    • Experimental results on CMU PIE, CMU Multi-PIE, and LFW databases demonstrated the effectiveness of the proposed OPR method.
    • The stacked OPR model showed improved performance in handling significant pose variations compared to existing methods.
    • The OPR framework successfully integrated face alignment, pose correction, and representation.

    Conclusions:

    • The orthogonal Procrustes regression (OPR) model offers a robust solution for pose-invariant face recognition.
    • The stacked OPR effectively addresses challenges posed by non-linear variations in 2D face images.
    • The proposed method provides a practical and unified framework for advanced face recognition tasks.