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A Fechner multiscale local descriptor for face recognition.

Jinxiang Feng1, Jie Xu1,2, Yizhi Deng1

  • 1Guangdong University of Technology, Guangzhou, China.

The Journal of Supercomputing
|June 26, 2023
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Summary
This summary is machine-generated.

A novel Fechner multiscale local descriptor (FMLD) enhances face recognition by simulating human perception. This method improves accuracy across various challenging conditions and boosts convolutional neural network (CNN) performance.

Keywords:
Face recognitionFeature extractionFechner multiscale local descriptor (FMLD)Fechner's law

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Area of Science:

  • Computer Vision
  • Biometric Recognition
  • Machine Learning

Background:

  • Traditional feature extraction methods often struggle with variations in illumination, pose, and expression.
  • Human visual perception offers a powerful model for robust feature representation.
  • Fechner's law describes the relationship between physical stimuli and perceived intensity.

Purpose of the Study:

  • To introduce a new feature descriptor, the Fechner multiscale local descriptor (FMLD), inspired by Fechner's law.
  • To enhance face recognition accuracy by simulating human pattern perception.
  • To improve the performance of convolutional neural networks (CNNs) in face recognition tasks.

Main Methods:

  • FMLD employs multiscale local domains to capture structural facial features, simulating human perception of intensity differences.
  • It extracts magnitude and direction features using binary patterns, maintaining a close relationship between them.
  • Feature maps are fused into an overall histogram for comprehensive representation.

Main Results:

  • FMLD demonstrates robust performance in face recognition, effectively handling variations in illumination, pose, expression, and occlusion.
  • The descriptor significantly enhances the performance of CNNs when integrated.
  • The combined FMLD and CNN approach outperforms existing advanced descriptors.

Conclusions:

  • FMLD offers a novel and effective approach to feature extraction for face recognition by leveraging principles of human perception.
  • The descriptor's ability to capture intricate facial details and its compatibility with CNNs make it a valuable tool for biometric systems.
  • FMLD represents a significant advancement in addressing real-world challenges in face recognition.