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A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism.

Xiaobo Qi1, Chenxu Wu1, Ying Shi1,2

  • 1School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China.

Computational Intelligence and Neuroscience
|January 30, 2023
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Summary
This summary is machine-generated.

This study introduces an improved face recognition model using a convolutional neural network (CNN) with BiLSTM and attention mechanisms. The AB-FR model enhances facial feature extraction and achieves high accuracy across multiple datasets.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Face recognition technology faces challenges due to variations in light, posture, and background.
  • Existing models struggle with recognition rates when encountering these environmental factors.

Purpose of the Study:

  • To propose an enhanced face recognition model, AB-FR, that improves accuracy and robustness.
  • To leverage attention mechanisms and BiLSTM within a CNN framework for superior facial feature extraction.

Main Methods:

  • Developed an AB-FR model integrating a convolutional neural network (CNN) with BiLSTM and an attention mechanism.
  • Employed an attention mechanism to enhance feature extraction by integrating information across different channels.
  • Utilized BiLSTM to capture temporal characteristics from varied facial image angles or time points.

Main Results:

  • The AB-FR model demonstrated improved identification performance and robustness on public datasets (CASIA-FaceV5, LFW, MTFL, CNBC, ORL).
  • Achieved high accuracy rates: 99.35% (CASIA-FaceV5), 96.46% (LFW), 97.04% (MTFL), 97.19% (CNBC), and 96.79% (ORL).

Conclusions:

  • The proposed AB-FR model significantly enhances face recognition accuracy and robustness.
  • The integration of attention mechanisms and BiLSTM offers a promising approach for advanced facial recognition systems.