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Face verification across age progression.

Narayanan Ramanathan1, Rama Chellappa

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742-3275, USA. ramanath@umiacs.umd.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 2, 2006
PubMed
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Facial aging significantly impacts face recognition. This study introduces a Bayesian classifier to verify identity across age progression, achieving an 8.5% equal error rate for images up to nine years apart.

Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Human faces change significantly with age.
  • Existing face recognition systems are sensitive to illumination and pose, but aging effects are less understood.
  • Assessing identity across age progression is crucial for reliable biometrics.

Purpose of the Study:

  • To investigate how age progression affects face image similarity and identity verification confidence.
  • To develop a robust face verification system capable of handling age-related variations.
  • To quantify the impact of aging on face recognition accuracy.

Main Methods:

  • Development of a Bayesian age difference classifier for face image classification.
  • Implementation of preprocessing techniques to mitigate illumination and pose variations in age-separated images.

Related Experiment Videos

  • Face verification experiments using a database of 465 individuals with passport images.

    • Main Results:

    • The proposed system demonstrates effective face verification across significant age differences.
    • An equal error rate (EER) of 8.5% was achieved for face images separated by up to nine years.
    • The study quantifies the similarity of faces across age progression.

    Conclusions:

    • Facial aging is a critical factor affecting face recognition performance.
    • The developed Bayesian classifier and preprocessing methods enhance face verification accuracy across age progression.
    • This research contributes to more reliable and accurate biometric systems in real-world scenarios.