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Viewpoint-Consistent 3D Face Alignment.

Sergey Tulyakov, Laszlo A Jeni, Jeffrey F Cohn

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 15, 2017
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    This study introduces a novel 3D face alignment method using regression forests for accurate landmark estimation. It offers computational efficiency and robustness to 3D face rotation, improving viewpoint consistency.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Biometrics

    Background:

    • 2D face alignment methods struggle with depth variations and 3D rotations, leading to errors.
    • Existing 3D approaches using morphable models are computationally expensive and their viewpoint consistency is uncertain.

    Purpose of the Study:

    • To develop a computationally efficient and robust 3D face alignment method.
    • To directly estimate 3D face landmarks from single images, avoiding complex model fitting.

    Main Methods:

    • A regression forest-based algorithm is proposed, extending the cascade pipeline with a third dimension.
    • Direct estimation of 3D face landmarks bypasses the need for 3D morphable model fitting.
    • Introduced the Multi-PIE Viewpoint Consistent database for training and testing.

    Main Results:

    • The proposed method achieves viewpoint consistency robust to 3D face rotation.
    • Demonstrated simple yet effective head pose estimation.
    • Outperformed alternative approaches in empirical tests on viewpoint consistency.

    Conclusions:

    • The novel regression forest approach provides an efficient and robust solution for 3D face alignment.
    • Direct 3D landmark estimation enhances accuracy and consistency under varying head poses.
    • The method is suitable for applications requiring real-time 3D face analysis.