Feature Selection Using Adaboost for Face Expression Recognition

Abstract

We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based classifier, the Support Vector Machines (SVM) and identify the advantages of using AdaBoost over SVM in this context. We use histograms of Gabor and Gaussian derivative responses as the appearance features. We apply our approach to the face expression recognition problem where local appearances play an important role. Finally, we show that though SVM performs equally well, AdaBoost feature selection provides a final hypothesis model that can easily be visualized and interpreted, which is lacking in the high dimensional support vectors of the SVM.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA438800

Entities

People

  • Allen R. Hanson
  • Deepak R. Karuppiah
  • Piyanuch Silapachote

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Computer Science
  • Databases
  • Dimensionality Reduction
  • Eye
  • Feature Selection
  • Frequency
  • Hidden Markov Models
  • Histograms
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Models
  • Orientation (Direction)
  • Recognition
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Speech Processing/Speech Recognition.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Space