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.
|Title of host publication||Engineering Applications of Neural Networks|
|Editors||Chrisina Jayne, Lazaros Iliadis|
|Place of Publication||Switzerland|
|ISBN (Print)||978-3-319-44187-0, 978-3-319-44188-7|
|Publication status||Published - 2016|
Bibliographical noteThe full text is not available on the repository.
- Emotion recognition
- Support Vector Machine
- Gabor filter
- Image classification
- Neural networks
- Social robots
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