PHOG特征与聚类特征选择的笑脸识别方法

基于Gabor特征的人脸表情识别系统虽具有良好的识别性能，但特征维数大、分类器复杂度高。因此，文中提出一种基于PHOG特征与聚类线性鉴别分析(CLDA)的笑脸识别方法。PHOG特征的引入在于简化系统的运算复杂度，而CLDA克服传统线性鉴别分析方法的多模态问题。实验结果表明PHOG特征免去Gabor特征在Adaboost耗时的特征选择过程，具有和Gabor特征相当或更优的识别性能，且CLDA在维数降低时，系统的识别率能得到更好保持。

Smile Recognition Based on PHOG Feature Extraction and Clustering Feature Selection

Gabor features are successfully applied to solve the problems of facial expression recognition.However,the dimension of Gabor features is usually too high to be practically applicable.A method based on Pyramid Histogram of Oriented Gradients(PHOG) feature and Clustering Linear Discriminate Analysis(CLDA) is proposed for smile expression recognition.The main merits of the proposed system are that the complexity can be decreased with low-dimension PHOG feature,and the multi-model problem can be overcome by CLDA.The experimental results show that system with PHOG feature achieves competitive or even higher recognition accuracy than with the Gabor feature,but with much lower of computation time cost.Moreover,the performance of CLDA does not be degraded significantly when decreasing the feature dimension.