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Face recognition algorithm using extended vector quantization histogram features.

Yan Yan1, Feifei Lee1, Xueqian Wu1

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.

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|January 3, 2018
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Summary
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This study introduces a novel face recognition algorithm combining vector quantization (VQ) and Markov stationary features (MSF). The enhanced method improves spatial information representation, outperforming existing techniques on benchmark face databases.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Vector Quantization (VQ) effectively generates facial features using codevector histograms.
  • VQ histogram features lack spatial structural information, limiting discrimination.
  • Existing face recognition methods face challenges in accurately capturing detailed facial structures.

Purpose of the Study:

  • To propose an improved face recognition algorithm by integrating Markov stationary features (MSF) with VQ.
  • To enhance VQ-based facial feature representation by incorporating spatial structural information.
  • To achieve superior face recognition performance compared to state-of-the-art methods.

Main Methods:

  • A hybrid approach combining Vector Quantization (VQ) for feature extraction and Markov stationary features (MSF) for spatial information.
  • VQ generates codevector histograms as initial facial representations.
  • MSF are utilized to extend VQ histograms, adding crucial spatial structural details.

Main Results:

  • The proposed algorithm demonstrates superior recognition accuracy on publicly available face databases.
  • The integration of MSF effectively addresses the limitations of VQ histogram features in capturing spatial information.
  • Performance surpasses several established state-of-the-art face recognition techniques.

Conclusions:

  • The combined VQ and MSF approach offers a robust and effective solution for face recognition.
  • Incorporating spatial structural information significantly enhances the discriminative power of facial features.
  • This algorithm represents a notable advancement in the field of automated face recognition.