Detail publikace

Combining Features for Recognizing Emotional Facial Expressions in Static Images

PŘINOSIL, J. SMÉKAL, Z. ESPOSITO, A.

Originální název

Combining Features for Recognizing Emotional Facial Expressions in Static Images

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

This work approaches the problem of recognizing emotional facial expressions in static images focusing on three preprocessing techniques for feature extraction, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Gabor filters. These methods are commonly used for face recognition and the novelty consists in combining features provided by them in order to improve the performance of an automatic procedure for recognizing emotional facial expressions. Classification performance experiments, testing new expressions and new subjects, were performed on the Japanese Female Facial Expression (JAFFE) database using a Multi-Layer Perceptron (MLP) Neural Network as classifier. The best classification performance on new expressions was obtained combining PCA and LDA features (93% of correct recognition rate), whereas that on new subjects was obtained combining PCA, LDA and Gabor filter features (94% of correct recognition rate).

Klíčová slova

Principal Component Analysis, Linear Discriminant Analysis, Gabor filters, facial features, basic emotions.

Autoři

PŘINOSIL, J.; SMÉKAL, Z.; ESPOSITO, A.

Rok RIV

2008

Vydáno

12. 12. 2008

Nakladatel

Springer

Místo

Berlin

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

2008

Číslo

5042

Stát

Spolková republika Německo

Strany od

59

Strany do

72

Strany počet

13

BibTex

@article{BUT49151,
  author="Jiří {Přinosil} and Zdeněk {Smékal} and Anna {Esposito}",
  title="Combining Features for Recognizing Emotional Facial Expressions in Static Images",
  journal="Lecture Notes in Computer Science",
  year="2008",
  volume="2008",
  number="5042",
  pages="59--72",
  issn="0302-9743"
}