一种新的多角度人脸表情识别方法

摘要

传统的多角度人脸表情识别方法是对角度特殊的样本采用角度特殊的分类器识别,该方法忽略了不同角度的人脸表情是相同的人脸表情的不同表现形式,而且传统的多角度人脸表情特征提取时间较长以及不能满足增量更新的要求,基于此,提出了一种新的多角度人脸表情识别方法.该方法首先提取回归模型的增量修正特征,然后用PCA进行特征选择,最后采用判别共享高斯过程隐变量模型识别多角度人脸表情.在CMU-PIE和LFPW数据库上的对比实验表明了该方法较传统的多角度人脸表情识别方法好.%The traditional multi-view facial expression recognition method is adopted view-specific classifier to recognition view-specific sample.This approach ignores the fact that different views of a facial expression are just different manifestations of the same facial expression.The traditional multi-view facial expression feature extraction which needs a lot of time and does not meet incremental update.To this end,this paper proposed a new method of multi-view facial expression recognition (MVFER).Firstly the MVFER system extracted regression model of incremental update feature,then it used PCA to select the feature,finally adopted discriminative shared Gaussian process latent variable model to recognition multi-view facial expression.The experiment carried on CMU-PIE database and LFPW database.The result shows the effectiveness of this method.