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Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Morphological Convolutional Neural Network for Efficient Facial Expression Recognition.

Robert1, Sarifuddin Madenda1, Suryadi Harmanto1

  • 1Department of Informatic Engineering, Gunadarma University, Depok 16424, Indonesia.

Journal of Imaging
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Morphological Convolutional Neural Network (MCNN) for facial expression recognition (FER). The MCNN integrates morphological operations with CNNs, improving accuracy and efficiency on diverse datasets.

Keywords:
convolutional neural networkdeep learningfacial expression recognitionmathematical morphologymulti-source datasetsubject-independent evaluation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Facial Expression Recognition (FER) models often rely on appearance features, making them vulnerable to variations in illumination and demographics.
  • Integrating structural information could complement appearance-based features for more robust FER.

Purpose of the Study:

  • To propose and evaluate a Morphological Convolutional Neural Network (MCNN) for enhanced facial expression recognition.
  • To investigate the utility of morphological structural representations in conjunction with convolutional features for FER.

Main Methods:

  • Developed a novel MCNN architecture combining morphological operations and CNN layers.
  • Created a diverse, multi-source, multi-ethnic FER dataset (3684 images, 326 subjects, 7 expression classes).
  • Employed subject-independent 10-fold cross-validation for rigorous model evaluation.

Main Results:

  • The MCNN2 variant achieved a top accuracy of 88.7%, outperforming or matching established models like MobileNetV2 (88.27%) and VGG19 (87.58%).
  • The proposed model demonstrated significant computational efficiency gains, with 21% lower inference latency and 64% reduced GPU memory usage.
  • Consistent performance improvements were observed across heterogeneous data conditions.

Conclusions:

  • Integrating morphological representations offers a modest yet consistent improvement in FER performance.
  • The MCNN architecture enhances generalization and computational efficiency for facial expression recognition tasks.