Facial action unit recognition using multi-class classification
Facial action unit recognition using multi-class classification
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DOI：
10.1016/j.neucom.2014.07.066
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发表时间：
2015-02
期刊：
影响因子：
6
通讯作者：
Raymond S. Smith;T. Windeatt
中科院分区：
文献类型：
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作者：
Raymond S. Smith;T. Windeatt
在使用面部动作编码系统（FACS）的面部表情分类的上下文中，我们解决了检测面部动作单元（AU）的问题。通过生成大量的多分辨率局部二进制模式（MLBP）特征，然后使用快速相关滤波（FCBF）从这些特征中进行选择来执行特征提取。通过训练单个纠错输出码（ECOC）多类分类器以生成若干Au组中的每一个的出现分数，避免了对每个Au的分类器的需要。提出了一种新的加权译码方案，其权值采用一阶沃尔什系数计算。使用Platt标度将ECOC评分校准为概率，并取适当的总和以单独获得每个Au的单独概率估计值。的分类器的偏差和方差属性的测量，我们表明，这两个来源的错误，可以减少通过增强ECOC通过自举和加权解码。
Within the context of facial expression classification using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). Feature extraction is performed by generating a large number of multi-resolution local binary pattern (MLBP) features and then selecting from these using fast correlation-based filtering (FCBF). The need for a classifier per AU is avoided by training a single error-correcting output code (ECOC) multi-class classifier to generate occurrence scores for each of several AU groups. A novel weighted decoding scheme is proposed with the weights computed using first order Walsh coefficients. Platt scaling is used to calibrate the ECOC scores to probabilities and appropriate sums are taken to obtain separate probability estimates for each AU individually. The bias and variance properties of the classifier are measured and we show that both these sources of error can be reduced by enhancing ECOC through bootstrapping and weighted decoding.