Herein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.

中文翻译：

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使用三阶段支持向量机的面部表情识别

在此，提出了一种用于面部表情识别的三阶段支持向量机（SVM）。第一阶段包括21个SVM，它们都是七个表达式的二进制组合。如果一个表达方式占主导地位，那么第一阶段就足够了；如果两个占主导地位，则使用第二阶段；如果三个占主导地位，则使用第三阶段。这些多级操作有助于最大程度地减少发生错误的可能性。使用不同的图像预处理阶段来确保从检测到的面部获得的特征对分类阶段具有有意义和适当的贡献。面部表情是面部肌肉运动的结果。这些微妙的运动通过面向直方图的渐变特征来检测，因为它对对象的形状敏感。然后将获得的功能用于训练三阶段SVM。使用了两种不同的验证方法：留一法和K折检验。在三个数据库（日本女性面部表情，扩展的Cohn-Kanade数据集和Radboud面部数据库）上的实验结果表明，与其他作品相比，该系统具有竞争力，并且具有更好的性能。