Emotion Recognition Using Facial Expression Images for a Robotic Companion

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

doi:10.1007/978-3-319-44188-7_6

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 proceeding › Chapter › peer-review

6 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 language	English
Title of host publication	Engineering Applications of Neural Networks
Editors	Chrisina Jayne, Lazaros Iliadis
Place of Publication	Switzerland
Publisher	Springer Verlag
Pages	79-93
Volume	629
ISBN (Print)	978-3-319-44187-0, 978-3-319-44188-7
DOIs	https://doi.org/10.1007/978-3-319-44188-7_6
Publication status	Published - 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

Access to Document

10.1007/978-3-319-44188-7_6

Cite this

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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.",

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author = "Ariel Ruiz-Garcia and Mark Elshaw and Abdulrahman Altahhan and Vasile Palade",

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T1 - Emotion Recognition Using Facial Expression Images for a Robotic Companion

AU - Ruiz-Garcia, Ariel

AU - Elshaw, Mark

AU - Altahhan, Abdulrahman

AU - Palade, Vasile

N1 - The full text is not available on the repository.

PY - 2016

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N2 - 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.

AB - 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.

KW - Emotion recognition

KW - Support Vector Machine

KW - Gabor filter

KW - Image classification

KW - Neural networks

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DO - 10.1007/978-3-319-44188-7_6

M3 - Chapter

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SN - 978-3-319-44188-7

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A2 - Iliadis, Lazaros

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Emotion Recognition Using Facial Expression Images for a Robotic Companion

Abstract

Bibliographical note

Keywords

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