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Related Experiment Video

Updated: Mar 8, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Longitudinal Study of Automatic Face Recognition.

Lacey Best-Rowden, Anil K Jain

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 17, 2017
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    Summary
    This summary is machine-generated.

    State-of-the-art face recognition systems show minimal degradation over time. Even after 6 years, 99% of subjects remain recognizable, demonstrating the permanence property of facial biometrics.

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    Area of Science:

    • Biometrics
    • Computer Vision
    • Forensic Science

    Background:

    • Automatic face recognition relies on uniqueness and permanence.
    • Previous studies suggest accuracy degrades over time, but lack large-scale statistical analysis.
    • Facial aging databases are crucial for understanding long-term recognition performance.

    Purpose of the Study:

    • Investigate the permanence property of face recognition systems.
    • Quantify the degradation of recognition accuracy with elapsed time.
    • Analyze the rate of decline in recognition ability over extended periods.

    Main Methods:

    • Analysis of two large-scale mugshot databases.
    • Application of mixed-effects regression models to genuine similarity scores.
    • Utilized state-of-the-art commercial off-the-shelf (COTS) face matchers.

    Main Results:

    • Despite decreasing genuine scores, 99% of subjects are recognizable up to 6 years at 0.01% False Accept Rate (FAR).
    • Age, sex, and race had only marginal influence on recognition trends over time.
    • Quantified population-mean rate of change, subject variability, and influencing factors.

    Conclusions:

    • Face recognition systems exhibit strong permanence, with minimal degradation over several years.
    • The study provides a statistical framework for evaluating facial aging effects on recognition.
    • Periodic re-evaluation is recommended to track evolving system capabilities and age-invariant properties.