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Periocular Data Fusion for Age and Gender Classification.

Carmen Bisogni1, Lucia Cascone1, Fabio Narducci1

  • 1Department of Computer Science, University of Salerno, I-84084 Fisciano, SA, Italy.

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Summary

Combining periocular features like pupils, fixations, and blinks enhances soft biometrics. This fusion approach achieved high accuracy in demographic classification by age and gender.

Keywords:
fusion strategiesmachine learningmultimodal fusionperiocular featuresprivacy

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Area of Science:

  • Biometrics and Security
  • Computer Vision
  • Machine Learning

Background:

  • Soft biometrics are gaining traction in security and business for individual identification.
  • Single soft biometric traits have limitations; combining multiple sources improves accuracy.
  • The periocular region offers a viable alternative for biometrics when lower facial data is unavailable.

Purpose of the Study:

  • To investigate the fusion of periocular features (pupils, fixations, blinks) for demographic classification (age and gender).
  • To evaluate different data fusion strategies at both feature and score levels.

Main Methods:

  • Feature-level fusion using a concatenation scheme.
  • Score-level fusion employing transformation and classifier-based methods (weighted sum, weighted product, Bayesian rule).
  • Utilized periocular data including pupils, fixations, and blinks for classification.

Main Results:

  • Achieved 84.45% accuracy for age classification.
  • Achieved 84.62% accuracy for gender classification.
  • Demonstrated the effectiveness of data fusion for enhancing soft biometric performance.

Conclusions:

  • Fusion of periocular soft biometrics is effective for demographic classification.
  • The proposed method balances privacy protection with strong discriminatory power.
  • Encourages further research into periocular biometrics for applications where lower facial data is obscured.