Descripción
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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. | |
Internacional
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Si |
DOI
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10.1109/ICPR.2008.4761731 |
Edición del Libro
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0 |
Editorial del Libro
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IEEE |
ISBN
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978-1-4244-2174-9 |
Serie
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Título del Libro
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Pattern Recognition, 2008. ICPR 2008. 19th International Conference on |
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