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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Development of a Universal Validation Protocol and an Open-Source Database for Multi-Contextual Facial Expression

Ludovica La Monica1, Costanza Cenerini2, Luca Vollero1

  • 1Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, 00128 Rome, Italy.

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|October 28, 2023
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Summary

This study introduces a universal method for validating facial expression recognition (FER) algorithms using the FeelPix dataset. This approach enhances the accuracy of emotion identification in diverse applications.

Keywords:
affective computingfacial expression recognitionfacial landmarkslabelled data databasemachine-learning algorithms

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Psychology

Background:

  • Facial expression recognition (FER) is complex due to variations in facial morphology, lighting, and cultural differences.
  • Existing FER algorithms require robust validation methods to ensure accuracy and reliability.

Purpose of the Study:

  • To develop a universal validation methodology for assessing FER algorithm performance.
  • To introduce the FeelPix dataset for training and testing FER algorithms.
  • To enable accurate emotion identification from facial expressions.

Main Methods:

  • A web application was developed for subjects to respond to emotive images.
  • Facial landmark coordinates were used to generate the labeled FeelPix dataset.
  • A computationally lightweight testing algorithm was designed for emotion classification.

Main Results:

  • The FeelPix dataset provides labeled data for training and testing FER algorithms.
  • The developed methodology offers a reliable way to validate FER algorithm performance.
  • The lightweight testing algorithm accurately classifies emotions based on FeelPix data.

Conclusions:

  • This work improves facial expression recognition accuracy and interpretation of emotions.
  • The methodology and dataset have broad implications for healthcare, security, HCI, and entertainment.
  • The developed solution is suitable for online systems due to its computational efficiency.