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Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
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Illumination compensation using oriented local histogram equalization and its application to face recognition.

Ping-Han Lee1, Szu-Wei Wu, Yi-Ping Hung

  • 1MediaTek Inc., Hsinchu 30078, Taiwan. pinghanlee@ntu.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces orientated local histogram equalization (OLHE) for face recognition, effectively compensating for varied lighting and utilizing edge orientation information. The new method achieves state-of-the-art results on benchmark datasets.

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Area of Science:

  • Computer Science
  • Image Processing
  • Artificial Intelligence

Background:

  • Illumination compensation and normalization are critical for robust face recognition systems.
  • Existing methods often focus on low-frequency illumination or high-frequency edges, neglecting edge orientation information.

Purpose of the Study:

  • To propose a novel algorithm, orientated local histogram equalization (OLHE), for enhanced face recognition.
  • To leverage edge orientation information alongside illumination compensation.

Main Methods:

  • Developed the orientated local histogram equalization (OLHE) technique.
  • Proposed three OLHE feature combination schemes for face recognition.
  • Evaluated performance on AR, CMU PIE, and extended Yale B datasets.

Main Results:

  • The proposed OLHE algorithm achieved state-of-the-art performance on multiple benchmark datasets.
  • Demonstrated effectiveness in handling extreme lighting conditions.
  • Showcased promising results in recognizing faces under varying illumination.

Conclusions:

  • Edge orientation is a valuable feature for face recognition.
  • The OLHE algorithm offers a significant improvement over existing methods for illumination-invariant face recognition.