3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns
ICPR(2012)
摘要
In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each aligned 3D facial surface is compactly represented as multiple global histograms of local normal patterns from multiple normal components and multiple binary encoding scales, namely Multi-Scale Local Normal Patterns (MS-LNPs). 3D facial expression recognition is finally carried out by modeling multiple kernel learning (MKL) to efficiently embed and combine these histogram based features. By using the SimpleMKL algorithm with the chi-square kernel, we achieved an average recognition rate of 80.14% based on a fair experimental setup. To the best of our knowledge, our method outperforms most of the state-of-the-art ones.
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关键词
image coding,face recognition,multiple normal components,learning (artificial intelligence),ms-lnp,multiple kernel learning,fully automatic approach,histogram based features,emotion recognition,feature extraction,multiscale local normal patterns,multiple binary encoding scales,global histograms,simplemkl algorithm,chi-square kernel,person-independent 3d facial expression recognition,discriminative expression feature extraction,3d facial surface,learning artificial intelligence
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