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Related Experiment Video

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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Component-based face recognition using statistical pattern matching analysis.

Sushil Kumar Paul1, Saida Bouakaz2, Chowdhury Mofizur Rahman3

  • 1Controller of Examinations, Bangladesh Technical Education Board, Agargaon, Dhaka, 1207 Bangladesh.

Pattern Analysis and Applications : PAA
|August 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fusion concept for component-based face recognition, achieving high accuracy despite variations in lighting, expression, and pose. The method utilizes statistical pattern matching for robust binary facial component analysis.

Keywords:
Binary facial componentFacial corner pointsHistogram linearization techniqueHu’s moment invariantsOtsu thresholdingProbability of white pixels

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Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Traditional face recognition methods struggle with variations in illumination, expression, pose, and occlusion.
  • Component-based approaches offer potential for improved robustness but require effective feature analysis.

Purpose of the Study:

  • To develop a fusion concept for component-based face recognition algorithms.
  • To create feature analysis methods for binary facial components (BFCs) invariant to common variations.
  • To enhance face recognition accuracy using statistical pattern matching techniques.

Main Methods:

  • Utilized Viola-Jones face detection and histogram linearization for preprocessing.
  • Extracted binary facial components (eyes, nose, mouth) using Otsu adaptive thresholding.
  • Developed statistical pattern matching tools: Chi-square (CSQ), Hu moment invariants (HuMIs), AbsDifPWPs, and GDVs.
  • Employed geometric distance values (GDVs) between facial corner points (FCPs) and pixel intensity values (PIVs).

Main Results:

  • The proposed fusion concept and statistical pattern matching tools demonstrated effectiveness on the BioID Face Database.
  • Achieved high accuracy and true positive rates, indicating robustness to variations.
  • The combination of CSQ, HuMIs, AbsDifPWPs, and GDVs proved beneficial for recognition.

Conclusions:

  • The developed fusion concept offers a robust approach to face recognition.
  • Statistical pattern matching of binary facial components is a viable strategy for invariant face recognition.
  • The method shows promise for real-world applications with challenging facial variations.