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Neonatal Face and Facial Landmark Detection from Video Recordings.

Ethan Grooby, Chiranjibi Sitaula, Soodeh Ahani

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    Summary

    This study introduces an automated method for neonatal face and landmark detection using YOLOv7Face. The advanced model achieves high accuracy, aiding in the development of non-contact, video-based infant health monitoring systems.

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    Area of Science:

    • Medical image analysis
    • Computer vision
    • Neonatal care technology

    Background:

    • Automated neonatal health assessment is crucial for early detection and intervention.
    • Video-based applications require accurate face and facial landmark detection for neonates.
    • Existing methods often lack the precision needed for clinical neonatal applications.

    Purpose of the Study:

    • To develop and evaluate an automated system for face and facial landmark detection in neonates.
    • To improve the accuracy and reliability of neonatal image analysis for health monitoring.
    • To establish a foundation for advanced video-based neonatal health assessment tools.

    Main Methods:

    • Utilized three public neonatal datasets (366 training, 89 testing images).
    • Applied transfer learning to YOLO-based models with data augmentation (flipping, color distortion, scaling, translation).
    • Explored image re-orientation and deep learning model fusion, focusing on YOLOv7Face.

    Main Results:

    • The YOLOv7Face model achieved 84.8% mean average precision for face detection.
    • Achieved a normalized mean error of 0.072 for facial landmark detection.
    • Outperformed existing methods in both face and landmark detection tasks.

    Conclusions:

    • The proposed YOLOv7Face model offers a significant advancement in automated neonatal face and landmark detection.
    • This technology supports the development of non-contact, automated neonatal health assessment algorithms.
    • Accurate detection is vital for applications like vital sign estimation, pain assessment, and jaundice detection.