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This study integrates multiple advanced techniques for robust facial expression recognition (FER). Combining pre-trained CNNs, MTCNN face detection, and Grad-CAM interpretability enhances system transparency and reliability.

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Area of Science:

  • Computer Vision
  • Affective Computing
  • Cognitive Neuroscience

Background:

  • Facial expressions are crucial for non-verbal communication.
  • Prior Facial Expression Recognition (FER) research focused on architecture or dataset optimization.
  • Few studies unified multiple advanced techniques into a single FER pipeline.

Purpose of the Study:

  • To propose a comprehensive FER pipeline integrating multiple advanced techniques.
  • To enhance the transparency and reliability of FER systems.
  • To bridge affective computing and cognitive neuroscience through FER and EEG signal analysis.

Main Methods:

  • Utilized multiple pre-trained Convolutional Neural Networks (CNNs).
  • Employed Multi-Task Convolutional Neural Network (MTCNN)-based face detection for precise facial region localization.
  • Incorporated Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability.
  • Evaluated DenseNet121, ResNet-50, ResNet18, and MobileNetV2 on Raf-DB and Cleaned-FER2013 datasets.

Main Results:

  • The integrated pipeline showed consistent improvements in interpretability and system robustness.
  • MTCNN face detection enhanced facial localization quality.
  • Transfer learning and interpretability techniques significantly boosted FER system transparency and reliability.
  • Demonstrated potential for combining FER with EEG for enhanced brain-computer interfaces.

Conclusions:

  • Integrating face detection, transfer learning, and interpretability techniques is effective for robust FER.
  • The proposed approach enhances FER system transparency and reliability.
  • This work advances brain-computer interfaces by combining facial expression and EEG analysis.