基于集成分类非定标摄像机单目表情识别的研究

Research on Monocular Expression Recognition Based on Integrated Classification Uncalibrated Camera

提出一个从一非定标摄像机单目视频中对人的面部表情自动识别的方法,通过一种类似面部肌肉分布的弹性模板来获得表情特征,完成相关规则化处理,随着一个表情的产生,这些特征在时空中变化形成的相应时间序列被逐行排列成矩阵,这个矩阵通过一种保持邻域嵌入(NPE)的流行学习方法被降维,通过一种集成分类器HCRF+SVM来识别这种包含表情特征信息的矩阵,利用相关数据库进行测试。结果表明,该方法比用单个分类器(HCRF或SVM)直接从二维面部表情进行特征识别效率更高,比典型的Kotsia方法更鲁棒。

An automatic recognition framework for human facial expressions from a monocular video with an uncalibrated camera is proposed.The expression characteristics are first acquired from a kind of deformable template,which is similar to a facial muscle distribution.After associated regularization,the time sequences from the trait changes in space-time under complete expressional production are then arranged line by line in a matrix.Next,the matrix dimensionality is reduced by a method of manifold learning of neighborhood-preserving embedding.Finally,the refined matrix containing the expression trait information is recognized by a classifier that integrates the hidden conditional random field(HCRF)or support vector machine(SVM).The proposed method shows a comparatively higher recognition rate than the individual HCRF or SVM methods in direct recognition from two-dimensional human face traits,and it is shown to be more robust than the typical Kotsia method.