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Contactless and pose invariant biometric identification using hand surface.

Vivek Kanhangad1, Ajay Kumar, David Zhang

  • 1The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 22, 2011
PubMed
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This study introduces a new method for hand matching using 3D imaging to overcome pose variations. The dynamic fusion of multimodal features significantly improves biometric system accuracy, even with unconstrained imaging.

Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Hand-based biometrics are susceptible to variations in hand pose.
  • Existing methods struggle with unconstrained and contact-free imaging scenarios.

Purpose of the Study:

  • To develop a novel hand matching approach robust to significant pose variations.
  • To enhance the performance of hand-based biometric systems using multimodal 3D and 2D data.

Main Methods:

  • Simultaneous acquisition of intensity and range images using a 3D digitizer.
  • 3D pose determination and normalization of acquired hand images.
  • Extraction of multimodal palmprint and hand geometry features from normalized 3D hand data.
  • Dynamic fusion strategy for combining individual matching scores.

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Main Results:

  • Achieved significant performance improvements in hand matching despite large pose variations.
  • Demonstrated consistent performance gains across various hand features with pose correction.
  • The dynamic fusion strategy improved performance by 60% (EER) compared to weighted sum fusion.

Conclusions:

  • The proposed approach is effective for unconstrained, contact-free hand-based biometric systems.
  • Pose normalization and dynamic fusion are crucial for robust hand matching.
  • Multimodal feature extraction from textured 3D hands enhances biometric accuracy.