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Biometric Identification from Human Aesthetic Preferences.

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  • 1Department of Computer Science, University of Calgary, 2500 University Dr. N.W., Calgary, AB T2N1N4, Canada.

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Summary
This summary is machine-generated.

This study introduces a new method for identifying individuals using their visual aesthetic preferences, achieving 95% accuracy. This behavioral biometric approach reduces data dimensions for improved person identification in online activities.

Keywords:
behavioral biometricsbiometric securitygene expression programminghuman–machine interactionspattern recognitionvisual aesthetics

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Area of Science:

  • Computer Science
  • Biometrics
  • Human-Computer Interaction

Background:

  • Human-machine interactions are increasingly prevalent across personal and professional life.
  • Person identification based on behavioral traits, rather than physical characteristics, is a growing research area.
  • Online behaviors, such as image liking, offer insights into individual preferences and can be used for identification.

Purpose of the Study:

  • To introduce a novel method for reducing the dimensionality of aesthetic features for improved person identification.
  • To explore the use of visual aesthetic as a behavioral biometric for identity recognition.
  • To enhance the accuracy and efficiency of person identification systems.

Main Methods:

  • A novel method for aesthetic feature dimensionality reduction using gene expression programming (GEP).
  • A tree-based genetic approach for feature recombination within the GEP framework.
  • Evaluation of the proposed system on a dataset of 200 Flickr users and 40,000 images.

Main Results:

  • Demonstrated high accuracy (95%) in identity recognition based solely on users' aesthetic preferences.
  • Successfully reduced feature dimensionality, leading to improved classifier accuracy.
  • Showcased reduced computation runtime and minimized storage requirements.

Conclusions:

  • Visual aesthetic preferences can be effectively utilized as a behavioral biometric for accurate person identification.
  • Gene expression programming offers a viable approach for aesthetic feature dimensionality reduction.
  • The proposed method significantly enhances the efficiency and accuracy of identity recognition systems in digital environments.