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3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns | IEEE Conference Publication | IEEE Xplore

3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns


Abstract:

In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each...Show More

Abstract:

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.
Date of Conference: 11-15 November 2012
Date Added to IEEE Xplore: 14 February 2013
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Conference Location: Tsukuba, Japan

References

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