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Related Experiment Video

Updated: Oct 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Multilabel convolution neural network for facial expression recognition and ordinal intensity estimation.

Olufisayo Ekundayo1, Serestina Viriri1

  • 1Computer Science Discipline, University of KwaZulu-Natal, Durban, South Africa.

Peerj. Computer Science
|December 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Multilabel Convolution Neural Network (ML-CNN) for facial expression recognition (FER) and intensity estimation. The ML-CNN model effectively recognizes emotions and their intensities simultaneously, outperforming existing methods.

Keywords:
Binary cross-entropyFacial expression recognitionIsland lossMultilabelOrdinal intensity estimation

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Area of Science:

  • Affective computing
  • Computer vision
  • Machine learning

Background:

  • Facial Expression Recognition (FER) is crucial for affective computing but often overlooks emotion intensity.
  • Existing methods for expression intensity estimation are limited, either assigning nominal values or classifying within intervals.
  • Current approaches for simultaneous emotion and intensity prediction lack a natural heuristic manner.

Purpose of the Study:

  • To develop a Multilabel Convolution Neural Network (ML-CNN) model for simultaneous emotion recognition and intensity estimation.
  • To enhance the ML-CNN model with Binary Cross-Entropy (BCE) loss and Island Loss (IL) to minimize variations.
  • To pre-train the ML-CNN model with Visual Geometric Group (VGG-16) for overfitting control.

Main Methods:

  • A Multilabel Convolution Neural Network (ML-CNN) architecture was employed.
  • The model was enhanced using a combination of Binary Cross-Entropy (BCE) loss and Island Loss (IL).
  • Pre-training with the VGG-16 network was utilized to mitigate overfitting.

Main Results:

  • The ML-CNN model demonstrated simultaneous prediction of emotion and intensity using ordinal metrics.
  • Experiments on the Binghampton University 3D Facial Expression (BU-3DFE) and Cohn Kanade extension (CK+) datasets showed strong performance.
  • The proposed ML-CNN model achieved superior performance compared to Chain Classifier (CC), RAKEL, MLKNN, and MLARAM algorithms.

Conclusions:

  • The developed ML-CNN model effectively recognizes emotions and estimates their intensity simultaneously.
  • The proposed loss functions (BCE and IL) contribute to minimizing intraclass and interclass variations.
  • The ML-CNN model offers a robust and superior solution for advanced facial expression analysis.