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- W2550876775 abstract "In this paper, we carried out a modular approach human 3D face recognition across neutral and six basic facial expressions experiments. Initially, a face model is decomposed into several modules before the 3D facial points for each of the modules are extracted. Three sizes of modules are used in our experiments: 2-Module, 6-Module and 10-Module. We apply Support Vector Machines as the classifier to each of the modules. A Majority Voting Scheme (MVS) and Weighted Voting Scheme (WVS) are constructed to infer the emotion underlying a collection of modules. From the analysis, we conclude that 10-Module outperforms 2-Module and 6-Module. In addition, the modules with low amount feature vectors and only contain boundary feature vectors perform worst." @default.
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- W2550876775 date "2014-12-01" @default.
- W2550876775 modified "2023-10-16" @default.
- W2550876775 title "A modular approach and voting scheme on 3D face recognition" @default.
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- W2550876775 doi "https://doi.org/10.1109/ispacs.2014.7024451" @default.
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