摘要
为了克服极限学习机(ELM)稳定性差、识别率不高的缺陷,利用支持向量机(SVM)一对一投票式分类算法准确度高的优势,提出一种改进的表情识别方法.该方法将一对一分类算法和ELM算法相结合形成一个新的算法即OAO-ELM(One-Against-One-Extreme Learning Machine),首先,对样本采用一对一的分类并利用ELM训练成一个弱分类器,然后,将这些弱分类器组合成一个最终的强分类器.预测结果,采用投票方式.用Gabor滤波提取表情特征,由于提取后特征维度很高,冗余大,引入主成分分析(PCA)来降维.基于JAFFE数据库实验结果表明,该算法在人脸表情识别上具有较高分类识别率和稳定性.
By using a vote of one-against-one Support Vector Machine advantages of high classification algorithm accuracy, an improved expression recognition method was proposed in order to modify the Extreme Learning Machine＇s disadvantage of bad stability and poor classification accuracy. The method combines one-against-one classification algorithm with Extreme Learning Machine, which are consist of a new algorithm--OAO-ELM. First, the algorithm uses the ELM process classification as weak classifier when training sample by one-against-one. Then, these weak classifiers are combined into the finally strong classification. Prediction the results of classification, by votes to the class. Gabor facial expressional features, since the high-dimensional Gabor features are redundant; The dimensional principal component analysis is used to select these features. Experimental results based on the JAFFE database show that it obtains higher accuracy and better stability.
出处
《计算机系统应用》
2015年第10期186-190,共5页
Computer Systems & Applications
