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Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization.

Shahenda Sarhan1,2, Aida A Nasr3, Mahmoud Y Shams3

  • 1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Computational Intelligence and Neuroscience
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Summary
This summary is machine-generated.

This study introduces a novel combined adaptive deep learning vector quantization (CADLVQ) classifier for multipose face recognition. The new method enhances accuracy and robustness in security applications.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Multipose face recognition is a significant challenge in security applications.
  • Existing research focuses on improving face detectors or recognition systems like Support Vector Machines and Deep Convolutional Neural Networks.
  • Limitations exist in current multipose face recognition techniques, necessitating advanced solutions.

Purpose of the Study:

  • To propose a novel combined adaptive deep learning vector quantization (CADLVQ) classifier for multipose face recognition.
  • To enhance the performance of adaptive deep learning vector quantization classifiers by integrating majority voting and Speeded Up Robust Features.
  • To evaluate the proposed CADLVQ classifier's effectiveness in multipose face recognition tasks.

Main Methods:

  • A combined adaptive deep learning vector quantization (CADLVQ) classifier was developed.
  • The majority voting algorithm was integrated with the Speeded Up Robust Feature (SURF) extractor to address weaknesses in existing adaptive deep learning vector quantization classifiers.
  • Performance was evaluated using metrics such as sensitivity, specificity, precision, and accuracy.

Main Results:

  • The proposed CADLVQ classifier demonstrated promising results in sensitivity, specificity, precision, and accuracy.
  • Experimental results showed superior performance compared to recent approaches in deep learning, statistical, and classical neural networks.
  • The confusion matrix analysis confirmed the reliability and robustness of the proposed system.

Conclusions:

  • The combined adaptive deep learning vector quantization (CADLVQ) classifier offers a significant advancement for multipose face recognition systems.
  • The integration of majority voting and SURF features effectively boosts classifier performance.
  • The proposed system represents a robust and reliable state-of-the-art solution for multipose face recognition in security applications.