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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Natural Facial Expression Recognition Using Dynamic and Static Schemes |
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Conference Article |
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Year |
2009 |
Publication |
5th International Symposium on Visual Computing |
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Volume |
5875 |
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Pages |
730–739 |
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Abstract |
Affective computing is at the core of a new paradigm in HCI and AI represented by human-centered computing. Within this paradigm, it is expected that machines will be enabled with perceiving capabilities, making them aware about users’ affective state. The current paper addresses the problem of facial expression recognition from monocular videos sequences. We propose a dynamic facial expression recognition scheme, which is proven to be very efficient. Furthermore, it is conveniently compared with several static-based systems adopting different magnitude of facial expression. We provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). We also provide performance evaluations using arbitrary test video sequences. |
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Las Vegas, USA |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-10330-8 |
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ISVC |
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Notes |
OR;MV |
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no |
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Call Number |
BCNPCL @ bcnpcl @ RaD2009 |
Serial |
1257 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing |
Type |
Conference Article |
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Year |
2009 |
Publication |
5th International Symposium on Visual Computing |
Abbreviated Journal |
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Volume |
5875 |
Issue |
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Pages |
44–55 |
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Abstract |
In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. |
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Address |
Las Vegas, USA |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-10330-8 |
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ISVC |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ ATR2009a |
Serial |
1246 |
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Permanent link to this record |