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Siamese Architecture-Based 3D DenseNet with Person-Specific Normalization Using Neutral Expression for Spontaneous

Kunyoung Lee1, Eui Chul Lee2

  • 1Department of Computer Science, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul 03016, Korea.

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Summary

This study introduces a novel 3D convolutional neural network (CNN) model for classifying spontaneous versus posed smiles. By using a neutral facial expression as a reference, the model accurately distinguishes smile types and enables automated smile analysis.

Keywords:
3D CNNaffective computingautomatic facial expression analysisdeep metric learningsmile classification

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Area of Science:

  • Computer Vision
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Spontaneous and posed smiles exhibit distinct spatiotemporal facial muscle movement patterns.
  • Differentiating between these smile types is crucial for applications in psychology, human-computer interaction, and security.

Purpose of the Study:

  • To develop and evaluate a novel automated system for classifying spontaneous and posed smiles.
  • To leverage a 3D convolutional neural network (CNN) with a Siamese network architecture for smile classification.
  • To utilize a neutral facial expression as a reference input to enhance classification accuracy.

Main Methods:

  • A 3D CNN model employing a Siamese network architecture was developed for video classification of smiles.
  • A neutral facial expression was used as an anchor input to learn spatiotemporal differences.
  • Principal Component Analysis (PCA) was used for visualization and feature analysis to compare methods.

Main Results:

  • The proposed model effectively learns spatiotemporal differences between neutral and smiling expressions, overcoming individual appearance variations.
  • Utilizing a neutral expression as an anchor significantly improved classification accuracy compared to conventional genuine/imposter pair methods.
  • The system demonstrated the potential for fully automated classification of spontaneous and posed smiles.

Conclusions:

  • The developed Siamese network-based 3D CNN offers a robust and accurate method for distinguishing spontaneous from posed smiles.
  • Employing a neutral expression as a reference is a key innovation for improving smile classification models.
  • This approach facilitates the development of advanced, automated systems for analyzing human emotional expressions.