Memorias de investigación
Book chapters:
An Empirical Comparison of Graph-based Dimensionality Reduction Algorithms on Facial Expression Recognition Tasks
Year:2008

Research Areas
  • Artificial intelligence

Information
Abstract
Facial expression recognition is a topic of interest both in industry and academia. Recent approaches to facial expression recognition are based on mapping ex- pressions to low dimensional manifolds. In this paper we revisit various dimensionality reduction algorithms using a graph-based paradigm. We compare eight di- mensionality reduction algorithms on a facial expres- sion recognition task. For this task, experimental re- sults show that although Linear Discriminant Analysis (LDA) is the simplest and oldest supervised approach, its results are comparable to more flexible recent algo- rithms. LDA, on the other hand, is much simpler to tune, since it only depends on one parameter.
International
Si
10.1109/ICPR.2008.4761731
Book Edition
0
Book Publishing
IEEE
ISBN
978-1-4244-2174-9
Series
Book title
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
From page
1
To page
4
Participants

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Percepción Computacional y Robótica
  • Departamento: Inteligencia Artificial