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Related Experiment Videos

Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple

Chi Ho Chan1, Muhammad Atif Tahir, Josef Kittler

  • 1Center for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, United Kingdom. chiho.chan@surrey.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 23, 2013
PubMed
Summary
This summary is machine-generated.

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This study introduces a robust face recognition system that effectively handles image blur and varying illumination. The novel multiscale Local Phase Quantization (MLPQ) descriptor improves accuracy in real-world conditions.

Area of Science:

  • Computer Vision
  • Biometrics
  • Image Processing

Background:

  • Face recognition systems struggle with uncontrolled illumination and image blur.
  • Image degradation from blurring is a significant challenge often overlooked in face recognition research.
  • Degradation corrupts facial information and impacts image alignment, reducing recognition accuracy.

Purpose of the Study:

  • To develop a robust face recognition system resilient to blurring and illumination variations.
  • To introduce novel descriptors and fusion techniques for enhanced face recognition performance.
  • To improve face recognition accuracy in real-world, degraded image conditions.

Main Methods:

  • Proposed a novel blur-robust face image descriptor: multiscale Local Phase Quantization (MLPQ).

Related Experiment Videos

  • Employed regional computation of MLPQ within a component-based framework for misalignment insensitivity.
  • Combined regional features and MLPQ with Multiscale Local Binary Pattern (MLBP) using kernel fusion for illumination invariance.
  • Utilized Kernel Discriminant Analysis (KDA) for discriminative feature extraction and geometric normalizations for score combination.

    • Main Results:

    • The proposed MLPQ descriptor demonstrated effectiveness in handling image blur.
    • Kernel fusion of MLPQ and MLBP descriptors improved insensitivity to illumination variations.
    • The combined system achieved accuracy comparable to state-of-the-art methods on benchmark datasets.
    • Evaluated on Yale, Extended Yale B, FERET, FRGC 2.0, and LFW databases.

    Conclusions:

    • The developed approach significantly enhances face recognition robustness against blur and illumination.
    • The study highlights the importance of addressing image degradation in face recognition systems.
    • The findings offer insights into effective face representation, fusion methods, and their role in handling variable conditions.