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Facial Anomaly Appraisal Using Discrepancy Optimization-Driven Automatic Inpainting.

Abdullah Hayajneh, Erchin Serpedin, Mitchell A Stotland

    IEEE Journal of Biomedical and Health Informatics
    |May 12, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a fast AI system to detect and rate facial anomalies like cleft lip. It objectively measures facial normality, correlating 92% with human judgment.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Facial anomalies require objective and consistent assessment.
    • Current methods for evaluating facial abnormalities lack universal standardization.
    • Automated systems can aid in objective analysis of facial deformities.

    Purpose of the Study:

    • To develop a machine learning framework for detecting, localizing, and rating facial anomalies.
    • To establish an objective and universal measure for facial abnormalities.
    • To create a rapid, automated system for facial normality assessment.

    Main Methods:

    • Utilized an enhanced two-phase automatic inpainting for face normalization.
    • Employed knowledge distillation to estimate anomaly heatmaps for inpainting.
    • Used deep convolutional neural networks (CNNs) for feature extraction and comparison.
    • Generated a final heatmap for scoring facial normality.

    Main Results:

    • Achieved results comparable to state-of-the-art methods in normalization.
    • Demonstrated a processing time of less than one second per image.
    • Showcased high correlation (92%) between AI scores and human judgment.
    • Validated the model's ability to detect anomalies without requiring anomalous training data.

    Conclusions:

    • The proposed framework offers a fast and objective method for assessing facial anomalies.
    • The system is suitable for mobile application deployment due to its speed and efficiency.
    • This AI-driven approach provides reliable facial normality scoring, aligning with human perception.