Emotion Recognition Using Facial Expression Images for a Robotic Companion

Ariel Ruiz-Garcia, Mark Elshaw, Abdulrahman Altahhan, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

Social robots are gradually becoming part of society. However, social robots lack the ability to adequately interact with users in a natural manner and are in need of more human-like abilities. In this paper we present experimental results on emotion recognition through the use of facial expression images obtained from the KDEF database, a fundamental first step towards the development of an empathic social robot. We compare the performance of Support Vector Machines (SVM) and a Multilayer Perceptron Network (MLP) on facial expression classification. We employ Gabor filters as an image pre-processing step before classification. Our SVM model achieves an accuracy rate of 97.08 %, whereas our MLP achieves 93.5 %. These experiments serve as benchmark for our current research project in the area of social robotics.
Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks
EditorsChrisina Jayne, Lazaros Iliadis
Place of PublicationSwitzerland
PublisherSpringer Verlag
Pages79-93
Volume629
ISBN (Print)978-3-319-44187-0, 978-3-319-44188-7
DOIs
Publication statusPublished - 2016

Bibliographical note

The full text is not available on the repository.

Keywords

  • Emotion recognition
  • Support Vector Machine
  • Gabor filter
  • Image classification
  • Neural networks
  • Social robots

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    Ruiz-Garcia, A., Elshaw, M., Altahhan, A., & Palade, V. (2016). Emotion Recognition Using Facial Expression Images for a Robotic Companion. In C. Jayne, & L. Iliadis (Eds.), Engineering Applications of Neural Networks (Vol. 629, pp. 79-93). Switzerland: Springer Verlag. https://doi.org/10.1007/978-3-319-44188-7_6