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Related Experiment Video

Updated: May 19, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

A comparative study of human thermal face recognition based on Haar wavelet transform and local binary pattern.

Debotosh Bhattacharjee1, Ayan Seal, Suranjan Ganguly

  • 1Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.

Computational Intelligence and Neuroscience
|August 28, 2012
PubMed
Summary

This study compares two thermal infrared face recognition methods. Haar wavelet transform and Local Binary Patterns (LBP) with Principal Component Analysis (PCA) show promise for facial recognition using temperature variations.

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Last Updated: May 19, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Area of Science:

  • Biometrics
  • Computer Vision
  • Infrared Imaging

Background:

  • Thermal infrared (IR) imaging captures facial temperature variations, offering unique texture features for recognition.
  • Traditional visible-light face recognition can be affected by illumination changes.

Purpose of the Study:

  • To conduct a comparative study of two face recognition methods operating in the thermal spectrum.
  • To evaluate the effectiveness of Haar wavelet transform and Local Binary Patterns (LBP) for thermal face recognition.

Main Methods:

  • Haar wavelet transform: Processing images to create LL band and average of LH/HL/HH bands, forming a confidence matrix.
  • Local Binary Patterns (LBP): Extracting features from subimages, concatenating them, and applying Principal Component Analysis (PCA) for dimensionality reduction.
  • Classification: Utilizing multilayer feedforward neural networks and minimum distance classifiers.

Main Results:

  • Both Haar wavelet transform and LBP-based methods were evaluated on custom and Terravic Facial IR databases.
  • The study demonstrates the feasibility of using thermal IR images for face recognition.

Conclusions:

  • Thermal infrared imaging provides valuable texture features for face recognition.
  • Comparative analysis of different feature extraction and classification techniques in the thermal domain is crucial for advancing biometrics.