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An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine.

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This study introduces a faster multispectral palmprint recognition system (MPRS) using autoencoders and regularized extreme learning machines. The novel approach effectively reduces features for quicker human identification without sacrificing accuracy.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Multispectral palmprint recognition systems (MPRS) are crucial for human identification.
  • Improving MPRS accuracy and speed is an ongoing challenge.
  • Existing methods may struggle with feature dimensionality and computational efficiency.

Purpose of the Study:

  • To develop a novel, efficient, and accurate MPRS.
  • To reduce palmprint feature dimensionality without compromising recognition accuracy.
  • To enhance the speed of palmprint-based identification and verification.

Main Methods:

  • Region of interest (ROI) extraction using David Zhang's method.
  • Palmprint feature extraction via normalized Gist (NGist) descriptor.
  • Dimensionality reduction using an optimized autoencoder (AE).
  • Classification using a regularized extreme learning machine (RELM).

Main Results:

  • The proposed AE-RELM approach significantly improved recognition accuracy.
  • Feature dimensionality reduction led to faster processing speeds.
  • Experimental results on the MS-PolyU dataset demonstrated superior performance compared to state-of-the-art methods.
  • The system exhibited robustness and efficiency in palmprint recognition.

Conclusions:

  • The proposed autoencoder and regularized extreme learning machine approach offers a robust and efficient solution for multispectral palmprint recognition.
  • This method effectively balances speed and accuracy, making it suitable for practical human identification applications.
  • The study highlights the potential of deep learning techniques for enhancing biometric systems.