A solution for facial expression representation and recognition
| Autor: | Mylène Masson, Séverine Dubuisson, Franck Davoine |
|---|---|
| Přispěvatelé: | Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2002 |
| Předmět: |
business.industry
Dimensionality reduction Feature extraction [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering Feature selection Pattern recognition 02 engineering and technology Linear subspace Facial recognition system ComputingMethodologies_PATTERNRECOGNITION Signal Processing Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Electrical and Electronic Engineering business Classifier (UML) ComputingMilieux_MISCELLANEOUS Software Subspace topology Mathematics |
| Zdroj: | Signal Processing: Image Communication Signal Processing: Image Communication, Elsevier, 2002, 17 (9), pp.657-673 |
| ISSN: | 0923-5965 1879-2677 |
| DOI: | 10.1016/s0923-5965(02)00076-0 |
| Popis: | The design of a recognition system requires careful attention to pattern representation and classifier design. Some statistical approaches choose those features, in a d-dimensional initial space, which allow sample vectors belonging to different categories to occupy compact and disjoint regions in a low-dimensional subspace. The effectiveness of the representation subspace is then determined by how well samples from different classes can be separated. In this paper, we propose a feature selection process that sorts the principal components, generated by principal component analysis, in the order of their importance to solve a specific recognition task. This method provides a low-dimensional representation subspace which has been optimized to improve the classification accuracy. We focus on the problem of facial expression recognition to demonstrate this technique. We also propose a decision tree-based classifier that provides a ‘‘coarse-to-fine’’ classification of new samples by successive projections onto more and more precise representation subspaces. Results confirm, first, that the choice of the representation strongly influences the classification results, second that a classifier has to be designed for a specific representation. r 2002 Elsevier Science B.V. All rights reserved. |
| Databáze: | OpenAIRE |
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