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Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition.

Vianney Perez-Gomez1, Homero V Rios-Figueroa1, Ericka Janet Rechy-Ramirez1

  • 1Research Center in Artificial Intelligence, University of Veracruz, Sebastian Camacho No.5, Centro, Xalapa C.P. 91000, Mexico.

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Summary
This summary is machine-generated.

This study identifies optimal geometric features for accurate facial expression recognition. A genetic algorithm (GA) achieved the smallest feature set with high accuracy, reducing recognition time.

Keywords:
facial expression recognitionfacial geometric featuresfeature selection

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Selecting relevant geometric features is key for accurate classification of expressions.
  • Existing methods often involve large feature sets, impacting computational efficiency.

Purpose of the Study:

  • To identify and select optimal geometric features for classifying six basic facial expressions.
  • To compare feature selection methods for maximizing classification accuracy while minimizing feature set size.
  • To investigate the efficiency of reduced feature sets for facial expression recognition.

Main Methods:

  • Proposed an initial set of 89 normalized distances and angles from 22 facial landmarks, inspired by FACS and MPEG-4.
  • Applied Principal Component Analysis (PCA) and a Genetic Algorithm (GA) for feature selection.
  • Evaluated feature sets using four classifiers on the Bosphorus and UIVBFED datasets.

Main Results:

  • PCA yielded 39 features, while GA resulted in 47 features.
  • Achieved median accuracies of 86.62% on the Bosphorus dataset and 93.92% on the UIVBFED dataset.
  • The GA-derived feature set was the smallest among methods with comparable accuracy.

Conclusions:

  • The genetic algorithm provides an effective method for selecting a minimal yet accurate set of geometric features for facial expression recognition.
  • Reduced feature sets significantly decrease recognition time, enhancing real-time applications.
  • This research contributes to more efficient and accurate human-computer interaction systems.