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PubMed · 15971916

Learning from examples in the small sample case: face expression recognition.

Guodong Guo·Charles R Dyer
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Abstract

Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.

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AlgorithmsArtificial IntelligenceCluster AnalysisComputer GraphicsComputer SimulationFaceFemaleHumansImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalModels, BiologicalModels, StatisticalNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedPhotographyReproducibility of ResultsSample SizeSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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BibTeXRIS

Guodong Guo, Charles R Dyer. 2005. Learning from examples in the small sample case: face expression recognition.. https://doi.org/10.1109/tsmcb.2005.846658

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