Multi-view facial expression recognition using local appearance features
Multi-view facial expression recognition using local appearance features
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发表时间：
2012-11
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通讯作者：
Nikolas Hesse;Tobias Gehrig;Hua Gao;H. K. Ekenel
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作者：
Nikolas Hesse;Tobias Gehrig;Hua Gao;H. K. Ekenel
在本文中，我们提出了一个多视角的面部表情分类系统。该系统利用自动定位的面部地标周围提取的局部特征，使用姿势相关的主动外观模型。一个姿态依赖的集成支持向量机分类器分配给给定的样本的六个基本的表达类之一。在BU-3DFE数据库上进行了大量的实验，比较了归一化地标坐标、离散余弦变换、局部二进制模式和基于尺度不变特征变换的特征，以及用于分类的形状和外观特征的组合。我们评估了AAM拟合误差、F分数特征选择和表达强度水平对分类精度的影响。从归一化地标坐标和基于DCT的特征的组合中选择的特征导致74.1%的正确分类率，优于最先进的自动多视图表情识别系统。
In this paper, we present a multi-view facial expression classification system. The system utilizes local features extracted around automatically located facial landmarks using pose-dependent active appearance models. A pose-dependent ensemble of support vector machine classifiers assigns the given sample to one of the six basic expression classes. Extensive experiments have been conducted on the BU-3DFE database, comparing normalized landmark coordinates, discrete cosine transform, local binary patterns, and scale invariant feature transform based features, as well as combinations of shape and appearance features for classification. We evaluate the influence of AAM fitting errors, F-score feature selection, and expression intensity levels on classification accuracy. Features selected from a combination of normalized landmark coordinates and DCT-based features lead to a correct classification rate of 74.1%, outperforming automatic state-of-the-art multi-view expression recognition systems.