基于位移特征与随机森林的表情识别方法研究

Facial expression recognition based on displacement feature and random forest

为了有效地对图像序列进行面部表情识别,提出一种基于主动形状模型（active shape model,ASM）结合Lucas-Kanade（LK）光流法的方法提取位移特征,采用随机森林分类器对提取到的位移特征进行分类与识别。在Extended Cohn-Kanade（CK＋）人脸表情数据库上的实验表明,该特征提取方法能够很好地描述图像序列中所包含的表情信息和特征点运动变化信息,比常用的K-近邻、贝叶斯网络和支持向量机等分类器所表现出来的效果要好,其识别率达到95.1%。

In order to effectively recognize facial expression from image sequences,a new method is proposed which combines active shape model（ASM）with Lucas-Kanade（LK）optical flow to extract displacement feature,and random Forest classifier is applied for classifying and recognizing the displacement feature which effectively improves the classification results in facial expression recognition.The experiments on CK＋ facial expression database demonstrate that the method can effectively represent image sequences＇ expression information and motion information of feature points.Compared with conventional classifiers such as K-Nearest neighbors algorithm,Bayesian network and support vector machine,the higher performance can is achieved by the new method and its rate reached 95.1%.