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Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks.

Jun Wan, Zhihui Lai, Jing Li

    IEEE Transactions on Neural Networks and Learning Systems
    |January 8, 2021
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    This study introduces a novel deep network for facial landmark detection, improving feature representation and global shape constraints. The Multiorder Multiconstraint Deep Network (MMDN) enhances accuracy, especially for challenging facial poses and occlusions.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Heatmap regression is effective for facial landmark detection.
    • Existing methods lack high-order feature correlations and global shape constraints, limiting accuracy.

    Purpose of the Study:

    • To propose a Multiorder Multiconstraint Deep Network (MMDN) for enhanced facial landmark detection.
    • To improve feature representation and incorporate robust shape constraints.

    Main Methods:

    • Developed an Implicit Multiorder Correlating Geometry-aware (IMCG) model for multiorder spatial and channel correlations.
    • Introduced an Explicit Probability-based Boundary-adaptive Regression (EPBR) method for global shape constraints.
    • Integrated IMCG and EPBR into the MMDN architecture.

    Main Results:

    • MMDN generates more accurate boundary-adaptive landmark heatmaps.
    • The network effectively enhances shape constraints for predicted landmarks.
    • Achieved superior performance over state-of-the-art methods on benchmark datasets, particularly with pose variations and occlusions.

    Conclusions:

    • The proposed MMDN significantly advances facial landmark detection accuracy.
    • MMDN's multiorder correlations and explicit shape constraints are crucial for robust performance.
    • This approach offers a more powerful solution for challenging facial landmark detection scenarios.