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Hierarchical scale convolutional neural network for facial expression recognition.

Xinqi Fan1, Mingjie Jiang1, Ali Raza Shahid1,2

  • 1Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.

Cognitive Neurodynamics
|July 18, 2022
PubMed
Summary

This study introduces a novel Hierarchical Scale Convolutional Neural Network (HSNet) for improved facial expression recognition. The HSNet systematically utilizes multi-scale features, achieving state-of-the-art accuracy across diverse datasets.

Keywords:
Dilated inception blocksFacial expression recognitionFeature guided auxiliary learningHierarchical scale networkKnowledge transfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is crucial for human behavior analysis and human-machine interaction.
  • Existing methods often overlook systematic utilization of multi-scale features for enhanced accuracy.

Purpose of the Study:

  • To propose a Hierarchical Scale Convolutional Neural Network (HSNet) for facial expression recognition.
  • To systematically leverage kernel, network, and knowledge scales for improved feature extraction and recognition accuracy.

Main Methods:

  • Developed dilation Inception blocks to enhance kernel-scale feature extraction, inspired by facial action units and sparsity.
  • Implemented a feature-guided auxiliary learning approach to guide shallow layers using high-level semantic features.
  • Employed knowledge transfer learning to mimic human cognitive improvement through learned knowledge.

Main Results:

  • The proposed HSNet significantly boosted performance on lab-controlled, synthesized, and in-the-wild facial expression datasets.
  • Achieved state-of-the-art accuracy on most tested databases, demonstrating superior recognition capabilities.
  • Ablation studies confirmed the effectiveness of individual modules within the HSNet architecture.

Conclusions:

  • The HSNet provides a systematic approach to utilizing multi-scale information for facial expression recognition.
  • The method offers substantial performance improvements and sets a new benchmark in the field.
  • This work highlights the importance of hierarchical scale integration in deep learning models for complex recognition tasks.