3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns
3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns
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发表时间：
2012-11
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通讯作者：
Huibin Li;Liming Chen;Di Huang;Yunhong Wang;J. Morvan
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作者：
Huibin Li;Liming Chen;Di Huang;Yunhong Wang;J. Morvan
在本文中，我们提出了一个全自动的方法，独立于人的三维面部表情识别。为了提取区别性表情特征，每个对齐的3D人脸表面被复杂地表示为来自多个法线分量和多个二进制编码尺度的局部法线模式的多个全局直方图，即多尺度局部法线模式（MS-LNP）。最后，通过对多核学习（MKL）进行建模，有效地嵌入和联合收割机这些基于直方图的特征，从而实现3D人脸表情识别。通过使用具有卡方核的SimpleMKL算法，基于公平的实验设置，我们实现了80.14%的平均识别率。据我们所知，我们的方法优于大多数最先进的方法。
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.