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Parallel ensemble learning of convolutional neural networks and local binary patterns for face recognition.

Jialin Tang1, Qinglang Su2, Binghua Su3

  • 1Beijing Institute of Technology, Zhuhai 519088, China; City University of Macau, Macau, China.

Computer Methods and Programs in Biomedicine
|July 7, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel face recognition method combining convolutional neural networks (CNN) and local binary patterns (LBP) through parallel ensemble learning. The enhanced approach significantly improves accuracy and robustness against various challenges, also boosting pedestrian detection rates.

Keywords:
Convolutional Neural Networks (CNN)Ensemble learningFace recognitionLocal Binary Patterns (LBP)

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Single convolutional neural networks (CNNs) exhibit limited generalization for face recognition due to factors like illumination, expression, and posture variations.
  • Low pedestrian detection rates, particularly under occlusion, present a significant challenge in computer vision applications.

Purpose of the Study:

  • To develop an advanced face recognition method that overcomes the generalization limitations of single CNNs.
  • To enhance the accuracy and robustness of face recognition systems against diverse environmental and pose variations.
  • To improve pedestrian detection rates, especially in scenarios with occlusions, by integrating face recognition techniques.

Main Methods:

  • Feature extraction using the Local Binary Patterns (LBP) operator for facial texture analysis.
  • Multi-network deep feature extraction via 10 CNNs with 5 distinct architectures.
  • Ensemble learning with majority voting for final face recognition classification.

Main Results:

  • Achieved 100% recognition rate on the ORL dataset and 97.51% on the Yale-B dataset.
  • Demonstrated improved tolerance to illumination, expression, and posture changes, enhancing overall face recognition accuracy.
  • Integrated with a pedestrian detection model, the hybrid system improved detection rates by 11.2%.

Conclusions:

  • The proposed parallel ensemble learning method significantly outperforms existing face recognition techniques.
  • The hybrid approach offers a robust solution for both face recognition and pedestrian detection challenges.
  • This research contributes a more generalized and accurate model for real-world computer vision applications.